SIPaKMeD_Padding.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA is available.\n",
      "Number of GPUs available: 1\n",
      "GPU 0: PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "if tf.test.is_built_with_cuda():\n",
    "    print(\"CUDA is available.\")\n",
    "    \n",
    "    gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "    num_gpus = len(gpus)\n",
    "    print(f\"Number of GPUs available: {num_gpus}\")\n",
    "\n",
    "    for i, gpu in enumerate(gpus):\n",
    "        print(f\"GPU {i}: {gpu}\")\n",
    "else:\n",
    "    print(\"CUDA is not available.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import shutil\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "class DatasetDivision:\n",
    "    def __init__(self, root_dir=\"\", output_dir=\"\"):\n",
    "        self.root_dir = root_dir\n",
    "        self.output_dir = output_dir\n",
    "        print(\"Instance of the class created\")\n",
    "\n",
    "    def printnow(self, new_dir):\n",
    "        print(\"Just testing that the method calling is working \" + new_dir)\n",
    "\n",
    "    def divide_dataset(self, root_dir, output_dir):\n",
    "        self.root_dir = root_dir\n",
    "        self.output_dir = output_dir\n",
    "\n",
    "        if not os.path.exists(self.output_dir):\n",
    "            os.mkdir(self.output_dir)\n",
    "        for split in ['train', 'val', 'test']:\n",
    "            split_dir = os.path.join(self.output_dir, split)\n",
    "            if not os.path.exists(split_dir):\n",
    "                os.mkdir(split_dir)\n",
    "\n",
    "        class_folders = os.listdir(root_dir)\n",
    "        print(f\"Class folders found: {class_folders}\")\n",
    "\n",
    "        for class_folder in class_folders:\n",
    "            class_path = os.path.join(root_dir, class_folder)\n",
    "            cropped_dir = os.path.join(class_path, 'CROPPED')\n",
    "\n",
    "            if not os.path.exists(cropped_dir):\n",
    "                print(f\"Warning: The 'CROPPED' folder does not exist in {class_path}\")\n",
    "                continue\n",
    "\n",
    "            class_files = glob.glob(os.path.join(cropped_dir, '*.bmp'))\n",
    "            print(f\"Found {len(class_files)} images in {cropped_dir}\")\n",
    "\n",
    "            if len(class_files) == 0:\n",
    "                print(f\"No images found in {cropped_dir}. Skipping this class.\")\n",
    "                continue\n",
    "\n",
    "            # Pembagian pertama: Pisahkan 15% untuk pengujian, 85% untuk pelatihan dan validasi\n",
    "            train_and_valid, test = train_test_split(class_files, test_size=0.15, random_state=42)\n",
    "\n",
    "            # Pembagian kedua: Dari 85% yang tersisa, bagi menjadi 15% untuk validasi dan 70% untuk pelatihan\n",
    "            train, val = train_test_split(train_and_valid, test_size=0.15 / 0.85, random_state=42)\n",
    "\n",
    "            train_dir = os.path.join(self.output_dir, 'train', class_folder)\n",
    "            val_dir = os.path.join(self.output_dir, 'val', class_folder)\n",
    "            test_dir = os.path.join(self.output_dir, 'test', class_folder)\n",
    "\n",
    "            if not os.path.exists(train_dir):\n",
    "                os.mkdir(train_dir)\n",
    "            if not os.path.exists(val_dir):\n",
    "                os.mkdir(val_dir)\n",
    "            if not os.path.exists(test_dir):\n",
    "                os.mkdir(test_dir)\n",
    "\n",
    "            for frame_file in train:\n",
    "                shutil.copy(frame_file, os.path.join(train_dir, os.path.basename(frame_file)))\n",
    "            for frame_file in val:\n",
    "                shutil.copy(frame_file, os.path.join(val_dir, os.path.basename(frame_file)))\n",
    "            for frame_file in test:\n",
    "                shutil.copy(frame_file, os.path.join(test_dir, os.path.basename(frame_file)))\n",
    "\n",
    "            print(f\"Processed {class_folder}:\")\n",
    "            print(f\"  - Train files: {len(train)}\")\n",
    "            print(f\"  - Val files: {len(val)}\")\n",
    "            print(f\"  - Test files: {len(test)}\")\n",
    "\n",
    "        print('Dataset Division finished.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribusi Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instance of the class created\n",
      "Just testing that the method calling is working The new guy\n",
      "Class folders found: ['im_Dyskeratotic', 'im_Koilocytotic', 'im_Metaplastic', 'im_Parabasal', 'im_Superficial-Intermediate']\n",
      "Found 813 images in D:/ViuMe_Paper/dataset_ori\\im_Dyskeratotic\\CROPPED\n",
      "Processed im_Dyskeratotic:\n",
      "  - Train files: 569\n",
      "  - Val files: 122\n",
      "  - Test files: 122\n",
      "Found 825 images in D:/ViuMe_Paper/dataset_ori\\im_Koilocytotic\\CROPPED\n",
      "Processed im_Koilocytotic:\n",
      "  - Train files: 577\n",
      "  - Val files: 124\n",
      "  - Test files: 124\n",
      "Found 793 images in D:/ViuMe_Paper/dataset_ori\\im_Metaplastic\\CROPPED\n",
      "Processed im_Metaplastic:\n",
      "  - Train files: 555\n",
      "  - Val files: 119\n",
      "  - Test files: 119\n",
      "Found 787 images in D:/ViuMe_Paper/dataset_ori\\im_Parabasal\\CROPPED\n",
      "Processed im_Parabasal:\n",
      "  - Train files: 550\n",
      "  - Val files: 118\n",
      "  - Test files: 119\n",
      "Found 831 images in D:/ViuMe_Paper/dataset_ori\\im_Superficial-Intermediate\\CROPPED\n",
      "Processed im_Superficial-Intermediate:\n",
      "  - Train files: 581\n",
      "  - Val files: 125\n",
      "  - Test files: 125\n",
      "Dataset Division finished.\n"
     ]
    }
   ],
   "source": [
    "path_dir = \"D:/ViuMe_Paper/dataset_ori\"\n",
    "output_dir = \"D:/ViuMe_Paper/sipakmed_split\"\n",
    "datasetdiv = DatasetDivision(root_dir=path_dir, output_dir=output_dir)\n",
    "datasetdiv.printnow(\"The new guy\")\n",
    "datasetdiv.divide_dataset(root_dir=path_dir, output_dir=output_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing - Padding-Final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed 001_04.bmp in im_Dyskeratotic of train set\n",
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      "Processed 067_07.bmp in im_Dyskeratotic of train set\n",
      "Processed 067_09.bmp in im_Dyskeratotic of train set\n",
      "Processed 067_11.bmp in im_Dyskeratotic of train set\n",
      "Processed 067_12.bmp in im_Dyskeratotic of train set\n",
      "Processed 067_13.bmp in im_Dyskeratotic of train set\n",
      "Processed 067_15.bmp in im_Dyskeratotic of train set\n",
      "Processed 067_17.bmp in im_Dyskeratotic of train set\n",
      "Processed 067_18.bmp in im_Dyskeratotic of train set\n",
      "Processed 067_19.bmp in im_Dyskeratotic of train set\n",
      "Processed 067_20.bmp in im_Dyskeratotic of train set\n",
      "Processed 067_21.bmp in im_Dyskeratotic of train set\n",
      "Processed 068_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 068_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 068_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 069_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 069_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 069_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 069_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 069_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 069_06.bmp in im_Dyskeratotic of train set\n",
      "Processed 070_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 071_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 071_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 071_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 071_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 071_06.bmp in im_Dyskeratotic of train set\n",
      "Processed 072_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 072_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 072_06.bmp in im_Dyskeratotic of train set\n",
      "Processed 072_10.bmp in im_Dyskeratotic of train set\n",
      "Processed 072_11.bmp in im_Dyskeratotic of train set\n",
      "Processed 072_12.bmp in im_Dyskeratotic of train set\n",
      "Processed 072_13.bmp in im_Dyskeratotic of train set\n",
      "Processed 072_14.bmp in im_Dyskeratotic of train set\n",
      "Processed 072_15.bmp in im_Dyskeratotic of train set\n",
      "Processed 073_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 073_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 073_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 073_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 074_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 074_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 074_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 074_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 074_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 075_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 076_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 077_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 077_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 078_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 080_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 081_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 081_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 082_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 083_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 083_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 083_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 084_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 087_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 088_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 089_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 090_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 090_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 090_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 091_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 092_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 092_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 093_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 093_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 093_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 093_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 094_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 094_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 095_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 095_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 096_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 097_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 097_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 098_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 099_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 099_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 100_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 100_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 100_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 100_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 100_06.bmp in im_Dyskeratotic of train set\n",
      "Processed 101_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 101_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 101_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 101_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 101_06.bmp in im_Dyskeratotic of train set\n",
      "Processed 101_07.bmp in im_Dyskeratotic of train set\n",
      "Processed 101_09.bmp in im_Dyskeratotic of train set\n",
      "Processed 101_10.bmp in im_Dyskeratotic of train set\n",
      "Processed 101_11.bmp in im_Dyskeratotic of train set\n",
      "Processed 101_12.bmp in im_Dyskeratotic of train set\n",
      "Processed 101_15.bmp in im_Dyskeratotic of train set\n",
      "Processed 102_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 102_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 102_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 102_06.bmp in im_Dyskeratotic of train set\n",
      "Processed 102_09.bmp in im_Dyskeratotic of train set\n",
      "Processed 103_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 103_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 103_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 103_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 104_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 104_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 104_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 104_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 104_07.bmp in im_Dyskeratotic of train set\n",
      "Processed 104_09.bmp in im_Dyskeratotic of train set\n",
      "Processed 105_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 105_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 106_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 108_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 109_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 109_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 110_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 110_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 110_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 111_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 111_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 111_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 111_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 111_06.bmp in im_Dyskeratotic of train set\n",
      "Processed 111_07.bmp in im_Dyskeratotic of train set\n",
      "Processed 112_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 112_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 112_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 112_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 112_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 112_06.bmp in im_Dyskeratotic of train set\n",
      "Processed 112_08.bmp in im_Dyskeratotic of train set\n",
      "Processed 112_09.bmp in im_Dyskeratotic of train set\n",
      "Processed 113_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 113_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 113_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 114_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 114_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 114_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 114_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 114_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 114_06.bmp in im_Dyskeratotic of train set\n",
      "Processed 114_07.bmp in im_Dyskeratotic of train set\n",
      "Processed 114_10.bmp in im_Dyskeratotic of train set\n",
      "Processed 114_12.bmp in im_Dyskeratotic of train set\n",
      "Processed 114_13.bmp in im_Dyskeratotic of train set\n",
      "Processed 114_14.bmp in im_Dyskeratotic of train set\n",
      "Processed 115_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 115_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 115_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 115_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 116_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 116_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 117_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 117_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 117_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 119_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 120_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 121_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 121_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 122_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 122_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 123_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 124_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 124_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 124_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 125_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 125_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 126_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 127_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 127_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 128_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 129_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 130_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 130_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 131_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 132_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 132_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 133_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 133_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 134_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 134_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 134_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 135_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 135_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 136_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 136_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 137_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 137_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 138_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 138_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 138_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 138_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 139_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 139_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 139_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 139_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 141_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 141_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 141_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 141_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 141_07.bmp in im_Dyskeratotic of train set\n",
      "Processed 141_09.bmp in im_Dyskeratotic of train set\n",
      "Processed 141_10.bmp in im_Dyskeratotic of train set\n",
      "Processed 142_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 142_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 143_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 144_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 146_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 146_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 146_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 147_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 147_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 147_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 149_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 150_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 150_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 153_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 154_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 155_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 155_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 156_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 156_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 156_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 157_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 157_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 159_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 160_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 160_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 161_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 162_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 163_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 163_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 163_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 164_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 164_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 164_06.bmp in im_Dyskeratotic of train set\n",
      "Processed 164_07.bmp in im_Dyskeratotic of train set\n",
      "Processed 164_08.bmp in im_Dyskeratotic of train set\n",
      "Processed 164_09.bmp in im_Dyskeratotic of train set\n",
      "Processed 164_11.bmp in im_Dyskeratotic of train set\n",
      "Processed 164_12.bmp in im_Dyskeratotic of train set\n",
      "Processed 164_15.bmp in im_Dyskeratotic of train set\n",
      "Processed 164_16.bmp in im_Dyskeratotic of train set\n",
      "Processed 164_17.bmp in im_Dyskeratotic of train set\n",
      "Processed 164_19.bmp in im_Dyskeratotic of train set\n",
      "Processed 164_20.bmp in im_Dyskeratotic of train set\n",
      "Processed 164_22.bmp in im_Dyskeratotic of train set\n",
      "Processed 164_24.bmp in im_Dyskeratotic of train set\n",
      "Processed 165_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 165_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 166_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 166_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 166_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 166_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 166_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 166_06.bmp in im_Dyskeratotic of train set\n",
      "Processed 167_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 167_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 168_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 168_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 168_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 169_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 171_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 171_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 173_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 173_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 173_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 173_06.bmp in im_Dyskeratotic of train set\n",
      "Processed 173_07.bmp in im_Dyskeratotic of train set\n",
      "Processed 174_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 174_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 174_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 175_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 176_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 177_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 179_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 180_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 181_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 182_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 183_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 184_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 184_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 184_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 184_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 184_06.bmp in im_Dyskeratotic of train set\n",
      "Processed 185_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 185_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 186_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 186_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 187_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 188_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 189_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 189_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 189_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 191_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 192_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 192_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 193_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 194_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 194_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 194_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 195_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 195_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 196_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 197_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 198_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 198_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 198_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 198_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 198_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 199_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 199_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 199_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 199_06.bmp in im_Dyskeratotic of train set\n",
      "Processed 199_07.bmp in im_Dyskeratotic of train set\n",
      "Processed 201_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 202_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 203_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 203_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 203_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 203_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 203_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 204_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 204_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 205_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 205_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 206_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 207_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 208_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 209_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 209_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 210_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 210_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 211_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 211_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 211_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 211_07.bmp in im_Dyskeratotic of train set\n",
      "Processed 212_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 212_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 213_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 214_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 214_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 214_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 215_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 216_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 216_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 217_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 217_05.bmp in im_Dyskeratotic of train set\n",
      "Processed 217_06.bmp in im_Dyskeratotic of train set\n",
      "Processed 218_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 218_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 218_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 218_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 219_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 219_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 219_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 219_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 219_07.bmp in im_Dyskeratotic of train set\n",
      "Processed 219_08.bmp in im_Dyskeratotic of train set\n",
      "Processed 219_09.bmp in im_Dyskeratotic of train set\n",
      "Processed 219_10.bmp in im_Dyskeratotic of train set\n",
      "Processed 220_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 220_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 220_06.bmp in im_Dyskeratotic of train set\n",
      "Processed 221_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 221_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 221_04.bmp in im_Dyskeratotic of train set\n",
      "Processed 221_07.bmp in im_Dyskeratotic of train set\n",
      "Processed 223_01.bmp in im_Dyskeratotic of train set\n",
      "Processed 223_02.bmp in im_Dyskeratotic of train set\n",
      "Processed 223_03.bmp in im_Dyskeratotic of train set\n",
      "Processed 001_01.bmp in im_Koilocytotic of train set\n",
      "Processed 002_01.bmp in im_Koilocytotic of train set\n",
      "Processed 003_01.bmp in im_Koilocytotic of train set\n",
      "Processed 004_01.bmp in im_Koilocytotic of train set\n",
      "Processed 005_01.bmp in im_Koilocytotic of train set\n",
      "Processed 006_01.bmp in im_Koilocytotic of train set\n",
      "Processed 006_02.bmp in im_Koilocytotic of train set\n",
      "Processed 007_01.bmp in im_Koilocytotic of train set\n",
      "Processed 007_02.bmp in im_Koilocytotic of train set\n",
      "Processed 008_01.bmp in im_Koilocytotic of train set\n",
      "Processed 009_01.bmp in im_Koilocytotic of train set\n",
      "Processed 010_01.bmp in im_Koilocytotic of train set\n",
      "Processed 011_01.bmp in im_Koilocytotic of train set\n",
      "Processed 011_03.bmp in im_Koilocytotic of train set\n",
      "Processed 011_04.bmp in im_Koilocytotic of train set\n",
      "Processed 011_05.bmp in im_Koilocytotic of train set\n",
      "Processed 011_06.bmp in im_Koilocytotic of train set\n",
      "Processed 012_01.bmp in im_Koilocytotic of train set\n",
      "Processed 012_02.bmp in im_Koilocytotic of train set\n",
      "Processed 013_01.bmp in im_Koilocytotic of train set\n",
      "Processed 013_03.bmp in im_Koilocytotic of train set\n",
      "Processed 015_03.bmp in im_Koilocytotic of train set\n",
      "Processed 015_06.bmp in im_Koilocytotic of train set\n",
      "Processed 015_08.bmp in im_Koilocytotic of train set\n",
      "Processed 016_01.bmp in im_Koilocytotic of train set\n",
      "Processed 016_02.bmp in im_Koilocytotic of train set\n",
      "Processed 016_03.bmp in im_Koilocytotic of train set\n",
      "Processed 016_04.bmp in im_Koilocytotic of train set\n",
      "Processed 017_02.bmp in im_Koilocytotic of train set\n",
      "Processed 017_04.bmp in im_Koilocytotic of train set\n",
      "Processed 018_01.bmp in im_Koilocytotic of train set\n",
      "Processed 019_03.bmp in im_Koilocytotic of train set\n",
      "Processed 019_04.bmp in im_Koilocytotic of train set\n",
      "Processed 020_04.bmp in im_Koilocytotic of train set\n",
      "Processed 021_01.bmp in im_Koilocytotic of train set\n",
      "Processed 023_01.bmp in im_Koilocytotic of train set\n",
      "Processed 023_02.bmp in im_Koilocytotic of train set\n",
      "Processed 023_03.bmp in im_Koilocytotic of train set\n",
      "Processed 023_04.bmp in im_Koilocytotic of train set\n",
      "Processed 024_02.bmp in im_Koilocytotic of train set\n",
      "Processed 024_03.bmp in im_Koilocytotic of train set\n",
      "Processed 025_06.bmp in im_Koilocytotic of train set\n",
      "Processed 026_01.bmp in im_Koilocytotic of train set\n",
      "Processed 026_02.bmp in im_Koilocytotic of train set\n",
      "Processed 026_05.bmp in im_Koilocytotic of train set\n",
      "Processed 027_01.bmp in im_Koilocytotic of train set\n",
      "Processed 027_02.bmp in im_Koilocytotic of train set\n",
      "Processed 029_02.bmp in im_Koilocytotic of train set\n",
      "Processed 029_03.bmp in im_Koilocytotic of train set\n",
      "Processed 030_01.bmp in im_Koilocytotic of train set\n",
      "Processed 030_02.bmp in im_Koilocytotic of train set\n",
      "Processed 030_03.bmp in im_Koilocytotic of train set\n",
      "Processed 030_05.bmp in im_Koilocytotic of train set\n",
      "Processed 031_01.bmp in im_Koilocytotic of train set\n",
      "Processed 032_01.bmp in im_Koilocytotic of train set\n",
      "Processed 032_02.bmp in im_Koilocytotic of train set\n",
      "Processed 033_01.bmp in im_Koilocytotic of train set\n",
      "Processed 033_02.bmp in im_Koilocytotic of train set\n",
      "Processed 033_03.bmp in im_Koilocytotic of train set\n",
      "Processed 034_01.bmp in im_Koilocytotic of train set\n",
      "Processed 034_02.bmp in im_Koilocytotic of train set\n",
      "Processed 036_01.bmp in im_Koilocytotic of train set\n",
      "Processed 037_01.bmp in im_Koilocytotic of train set\n",
      "Processed 037_02.bmp in im_Koilocytotic of train set\n",
      "Processed 037_03.bmp in im_Koilocytotic of train set\n",
      "Processed 038_01.bmp in im_Koilocytotic of train set\n",
      "Processed 038_02.bmp in im_Koilocytotic of train set\n",
      "Processed 039_01.bmp in im_Koilocytotic of train set\n",
      "Processed 040_02.bmp in im_Koilocytotic of train set\n",
      "Processed 043_01.bmp in im_Koilocytotic of train set\n",
      "Processed 043_02.bmp in im_Koilocytotic of train set\n",
      "Processed 043_03.bmp in im_Koilocytotic of train set\n",
      "Processed 044_03.bmp in im_Koilocytotic of train set\n",
      "Processed 045_01.bmp in im_Koilocytotic of train set\n",
      "Processed 045_03.bmp in im_Koilocytotic of train set\n",
      "Processed 046_01.bmp in im_Koilocytotic of train set\n",
      "Processed 046_02.bmp in im_Koilocytotic of train set\n",
      "Processed 046_03.bmp in im_Koilocytotic of train set\n",
      "Processed 047_01.bmp in im_Koilocytotic of train set\n",
      "Processed 047_02.bmp in im_Koilocytotic of train set\n",
      "Processed 048_01.bmp in im_Koilocytotic of train set\n",
      "Processed 049_01.bmp in im_Koilocytotic of train set\n",
      "Processed 049_02.bmp in im_Koilocytotic of train set\n",
      "Processed 049_03.bmp in im_Koilocytotic of train set\n",
      "Processed 050_01.bmp in im_Koilocytotic of train set\n",
      "Processed 050_02.bmp in im_Koilocytotic of train set\n",
      "Processed 051_01.bmp in im_Koilocytotic of train set\n",
      "Processed 051_03.bmp in im_Koilocytotic of train set\n",
      "Processed 052_01.bmp in im_Koilocytotic of train set\n",
      "Processed 052_03.bmp in im_Koilocytotic of train set\n",
      "Processed 053_01.bmp in im_Koilocytotic of train set\n",
      "Processed 053_02.bmp in im_Koilocytotic of train set\n",
      "Processed 054_01.bmp in im_Koilocytotic of train set\n",
      "Processed 054_02.bmp in im_Koilocytotic of train set\n",
      "Processed 055_01.bmp in im_Koilocytotic of train set\n",
      "Processed 055_02.bmp in im_Koilocytotic of train set\n",
      "Processed 056_01.bmp in im_Koilocytotic of train set\n",
      "Processed 057_01.bmp in im_Koilocytotic of train set\n",
      "Processed 058_01.bmp in im_Koilocytotic of train set\n",
      "Processed 058_02.bmp in im_Koilocytotic of train set\n",
      "Processed 059_01.bmp in im_Koilocytotic of train set\n",
      "Processed 059_02.bmp in im_Koilocytotic of train set\n",
      "Processed 059_03.bmp in im_Koilocytotic of train set\n",
      "Processed 061_01.bmp in im_Koilocytotic of train set\n",
      "Processed 062_01.bmp in im_Koilocytotic of train set\n",
      "Processed 063_01.bmp in im_Koilocytotic of train set\n",
      "Processed 064_01.bmp in im_Koilocytotic of train set\n",
      "Processed 065_01.bmp in im_Koilocytotic of train set\n",
      "Processed 065_03.bmp in im_Koilocytotic of train set\n",
      "Processed 066_01.bmp in im_Koilocytotic of train set\n",
      "Processed 066_02.bmp in im_Koilocytotic of train set\n",
      "Processed 067_01.bmp in im_Koilocytotic of train set\n",
      "Processed 067_02.bmp in im_Koilocytotic of train set\n",
      "Processed 069_02.bmp in im_Koilocytotic of train set\n",
      "Processed 070_01.bmp in im_Koilocytotic of train set\n",
      "Processed 071_01.bmp in im_Koilocytotic of train set\n",
      "Processed 072_01.bmp in im_Koilocytotic of train set\n",
      "Processed 072_02.bmp in im_Koilocytotic of train set\n",
      "Processed 072_03.bmp in im_Koilocytotic of train set\n",
      "Processed 072_04.bmp in im_Koilocytotic of train set\n",
      "Processed 072_05.bmp in im_Koilocytotic of train set\n",
      "Processed 072_06.bmp in im_Koilocytotic of train set\n",
      "Processed 072_07.bmp in im_Koilocytotic of train set\n",
      "Processed 073_01.bmp in im_Koilocytotic of train set\n",
      "Processed 074_01.bmp in im_Koilocytotic of train set\n",
      "Processed 074_02.bmp in im_Koilocytotic of train set\n",
      "Processed 075_01.bmp in im_Koilocytotic of train set\n",
      "Processed 076_01.bmp in im_Koilocytotic of train set\n",
      "Processed 077_01.bmp in im_Koilocytotic of train set\n",
      "Processed 077_02.bmp in im_Koilocytotic of train set\n",
      "Processed 077_03.bmp in im_Koilocytotic of train set\n",
      "Processed 077_04.bmp in im_Koilocytotic of train set\n",
      "Processed 077_06.bmp in im_Koilocytotic of train set\n",
      "Processed 077_07.bmp in im_Koilocytotic of train set\n",
      "Processed 078_01.bmp in im_Koilocytotic of train set\n",
      "Processed 079_01.bmp in im_Koilocytotic of train set\n",
      "Processed 080_01.bmp in im_Koilocytotic of train set\n",
      "Processed 081_02.bmp in im_Koilocytotic of train set\n",
      "Processed 082_01.bmp in im_Koilocytotic of train set\n",
      "Processed 082_02.bmp in im_Koilocytotic of train set\n",
      "Processed 082_04.bmp in im_Koilocytotic of train set\n",
      "Processed 082_05.bmp in im_Koilocytotic of train set\n",
      "Processed 082_06.bmp in im_Koilocytotic of train set\n",
      "Processed 083_04.bmp in im_Koilocytotic of train set\n",
      "Processed 084_02.bmp in im_Koilocytotic of train set\n",
      "Processed 084_05.bmp in im_Koilocytotic of train set\n",
      "Processed 085_01.bmp in im_Koilocytotic of train set\n",
      "Processed 085_02.bmp in im_Koilocytotic of train set\n",
      "Processed 085_03.bmp in im_Koilocytotic of train set\n",
      "Processed 085_05.bmp in im_Koilocytotic of train set\n",
      "Processed 086_01.bmp in im_Koilocytotic of train set\n",
      "Processed 086_02.bmp in im_Koilocytotic of train set\n",
      "Processed 086_03.bmp in im_Koilocytotic of train set\n",
      "Processed 086_04.bmp in im_Koilocytotic of train set\n",
      "Processed 086_05.bmp in im_Koilocytotic of train set\n",
      "Processed 087_01.bmp in im_Koilocytotic of train set\n",
      "Processed 087_02.bmp in im_Koilocytotic of train set\n",
      "Processed 087_05.bmp in im_Koilocytotic of train set\n",
      "Processed 087_06.bmp in im_Koilocytotic of train set\n",
      "Processed 087_07.bmp in im_Koilocytotic of train set\n",
      "Processed 087_09.bmp in im_Koilocytotic of train set\n",
      "Processed 087_10.bmp in im_Koilocytotic of train set\n",
      "Processed 087_11.bmp in im_Koilocytotic of train set\n",
      "Processed 087_12.bmp in im_Koilocytotic of train set\n",
      "Processed 087_13.bmp in im_Koilocytotic of train set\n",
      "Processed 088_01.bmp in im_Koilocytotic of train set\n",
      "Processed 088_02.bmp in im_Koilocytotic of train set\n",
      "Processed 088_03.bmp in im_Koilocytotic of train set\n",
      "Processed 088_04.bmp in im_Koilocytotic of train set\n",
      "Processed 088_05.bmp in im_Koilocytotic of train set\n",
      "Processed 088_06.bmp in im_Koilocytotic of train set\n",
      "Processed 088_08.bmp in im_Koilocytotic of train set\n",
      "Processed 088_09.bmp in im_Koilocytotic of train set\n",
      "Processed 088_10.bmp in im_Koilocytotic of train set\n",
      "Processed 089_02.bmp in im_Koilocytotic of train set\n",
      "Processed 089_04.bmp in im_Koilocytotic of train set\n",
      "Processed 089_05.bmp in im_Koilocytotic of train set\n",
      "Processed 090_01.bmp in im_Koilocytotic of train set\n",
      "Processed 090_02.bmp in im_Koilocytotic of train set\n",
      "Processed 090_03.bmp in im_Koilocytotic of train set\n",
      "Processed 090_07.bmp in im_Koilocytotic of train set\n",
      "Processed 090_08.bmp in im_Koilocytotic of train set\n",
      "Processed 091_01.bmp in im_Koilocytotic of train set\n",
      "Processed 091_02.bmp in im_Koilocytotic of train set\n",
      "Processed 091_03.bmp in im_Koilocytotic of train set\n",
      "Processed 092_01.bmp in im_Koilocytotic of train set\n",
      "Processed 092_02.bmp in im_Koilocytotic of train set\n",
      "Processed 093_01.bmp in im_Koilocytotic of train set\n",
      "Processed 093_02.bmp in im_Koilocytotic of train set\n",
      "Processed 093_03.bmp in im_Koilocytotic of train set\n",
      "Processed 093_04.bmp in im_Koilocytotic of train set\n",
      "Processed 093_05.bmp in im_Koilocytotic of train set\n",
      "Processed 093_07.bmp in im_Koilocytotic of train set\n",
      "Processed 093_08.bmp in im_Koilocytotic of train set\n",
      "Processed 093_09.bmp in im_Koilocytotic of train set\n",
      "Processed 094_02.bmp in im_Koilocytotic of train set\n",
      "Processed 094_03.bmp in im_Koilocytotic of train set\n",
      "Processed 094_04.bmp in im_Koilocytotic of train set\n",
      "Processed 094_05.bmp in im_Koilocytotic of train set\n",
      "Processed 094_06.bmp in im_Koilocytotic of train set\n",
      "Processed 094_08.bmp in im_Koilocytotic of train set\n",
      "Processed 094_09.bmp in im_Koilocytotic of train set\n",
      "Processed 094_10.bmp in im_Koilocytotic of train set\n",
      "Processed 094_11.bmp in im_Koilocytotic of train set\n",
      "Processed 095_01.bmp in im_Koilocytotic of train set\n",
      "Processed 095_02.bmp in im_Koilocytotic of train set\n",
      "Processed 095_03.bmp in im_Koilocytotic of train set\n",
      "Processed 095_06.bmp in im_Koilocytotic of train set\n",
      "Processed 095_07.bmp in im_Koilocytotic of train set\n",
      "Processed 095_08.bmp in im_Koilocytotic of train set\n",
      "Processed 095_09.bmp in im_Koilocytotic of train set\n",
      "Processed 095_10.bmp in im_Koilocytotic of train set\n",
      "Processed 095_11.bmp in im_Koilocytotic of train set\n",
      "Processed 095_12.bmp in im_Koilocytotic of train set\n",
      "Processed 095_14.bmp in im_Koilocytotic of train set\n",
      "Processed 095_15.bmp in im_Koilocytotic of train set\n",
      "Processed 095_16.bmp in im_Koilocytotic of train set\n",
      "Processed 095_17.bmp in im_Koilocytotic of train set\n",
      "Processed 095_18.bmp in im_Koilocytotic of train set\n",
      "Processed 095_19.bmp in im_Koilocytotic of train set\n",
      "Processed 096_01.bmp in im_Koilocytotic of train set\n",
      "Processed 096_02.bmp in im_Koilocytotic of train set\n",
      "Processed 096_03.bmp in im_Koilocytotic of train set\n",
      "Processed 097_02.bmp in im_Koilocytotic of train set\n",
      "Processed 097_03.bmp in im_Koilocytotic of train set\n",
      "Processed 097_04.bmp in im_Koilocytotic of train set\n",
      "Processed 097_05.bmp in im_Koilocytotic of train set\n",
      "Processed 097_06.bmp in im_Koilocytotic of train set\n",
      "Processed 097_07.bmp in im_Koilocytotic of train set\n",
      "Processed 097_08.bmp in im_Koilocytotic of train set\n",
      "Processed 097_11.bmp in im_Koilocytotic of train set\n",
      "Processed 098_01.bmp in im_Koilocytotic of train set\n",
      "Processed 098_02.bmp in im_Koilocytotic of train set\n",
      "Processed 098_03.bmp in im_Koilocytotic of train set\n",
      "Processed 099_01.bmp in im_Koilocytotic of train set\n",
      "Processed 099_03.bmp in im_Koilocytotic of train set\n",
      "Processed 099_07.bmp in im_Koilocytotic of train set\n",
      "Processed 099_10.bmp in im_Koilocytotic of train set\n",
      "Processed 099_11.bmp in im_Koilocytotic of train set\n",
      "Processed 099_12.bmp in im_Koilocytotic of train set\n",
      "Processed 099_13.bmp in im_Koilocytotic of train set\n",
      "Processed 099_14.bmp in im_Koilocytotic of train set\n",
      "Processed 099_15.bmp in im_Koilocytotic of train set\n",
      "Processed 100_01.bmp in im_Koilocytotic of train set\n",
      "Processed 100_02.bmp in im_Koilocytotic of train set\n",
      "Processed 101_01.bmp in im_Koilocytotic of train set\n",
      "Processed 101_02.bmp in im_Koilocytotic of train set\n",
      "Processed 102_01.bmp in im_Koilocytotic of train set\n",
      "Processed 102_03.bmp in im_Koilocytotic of train set\n",
      "Processed 102_04.bmp in im_Koilocytotic of train set\n",
      "Processed 102_05.bmp in im_Koilocytotic of train set\n",
      "Processed 103_02.bmp in im_Koilocytotic of train set\n",
      "Processed 104_01.bmp in im_Koilocytotic of train set\n",
      "Processed 104_02.bmp in im_Koilocytotic of train set\n",
      "Processed 104_03.bmp in im_Koilocytotic of train set\n",
      "Processed 104_04.bmp in im_Koilocytotic of train set\n",
      "Processed 104_05.bmp in im_Koilocytotic of train set\n",
      "Processed 104_06.bmp in im_Koilocytotic of train set\n",
      "Processed 104_08.bmp in im_Koilocytotic of train set\n",
      "Processed 105_01.bmp in im_Koilocytotic of train set\n",
      "Processed 105_02.bmp in im_Koilocytotic of train set\n",
      "Processed 105_03.bmp in im_Koilocytotic of train set\n",
      "Processed 105_05.bmp in im_Koilocytotic of train set\n",
      "Processed 105_06.bmp in im_Koilocytotic of train set\n",
      "Processed 105_07.bmp in im_Koilocytotic of train set\n",
      "Processed 105_11.bmp in im_Koilocytotic of train set\n",
      "Processed 105_12.bmp in im_Koilocytotic of train set\n",
      "Processed 105_13.bmp in im_Koilocytotic of train set\n",
      "Processed 105_14.bmp in im_Koilocytotic of train set\n",
      "Processed 105_15.bmp in im_Koilocytotic of train set\n",
      "Processed 105_17.bmp in im_Koilocytotic of train set\n",
      "Processed 105_19.bmp in im_Koilocytotic of train set\n",
      "Processed 105_20.bmp in im_Koilocytotic of train set\n",
      "Processed 105_21.bmp in im_Koilocytotic of train set\n",
      "Processed 105_23.bmp in im_Koilocytotic of train set\n",
      "Processed 105_24.bmp in im_Koilocytotic of train set\n",
      "Processed 105_25.bmp in im_Koilocytotic of train set\n",
      "Processed 106_01.bmp in im_Koilocytotic of train set\n",
      "Processed 107_01.bmp in im_Koilocytotic of train set\n",
      "Processed 107_02.bmp in im_Koilocytotic of train set\n",
      "Processed 107_04.bmp in im_Koilocytotic of train set\n",
      "Processed 107_05.bmp in im_Koilocytotic of train set\n",
      "Processed 107_06.bmp in im_Koilocytotic of train set\n",
      "Processed 107_08.bmp in im_Koilocytotic of train set\n",
      "Processed 107_09.bmp in im_Koilocytotic of train set\n",
      "Processed 108_01.bmp in im_Koilocytotic of train set\n",
      "Processed 108_02.bmp in im_Koilocytotic of train set\n",
      "Processed 108_03.bmp in im_Koilocytotic of train set\n",
      "Processed 108_05.bmp in im_Koilocytotic of train set\n",
      "Processed 108_06.bmp in im_Koilocytotic of train set\n",
      "Processed 108_08.bmp in im_Koilocytotic of train set\n",
      "Processed 108_09.bmp in im_Koilocytotic of train set\n",
      "Processed 108_10.bmp in im_Koilocytotic of train set\n",
      "Processed 108_11.bmp in im_Koilocytotic of train set\n",
      "Processed 109_01.bmp in im_Koilocytotic of train set\n",
      "Processed 109_02.bmp in im_Koilocytotic of train set\n",
      "Processed 109_03.bmp in im_Koilocytotic of train set\n",
      "Processed 109_04.bmp in im_Koilocytotic of train set\n",
      "Processed 109_05.bmp in im_Koilocytotic of train set\n",
      "Processed 109_06.bmp in im_Koilocytotic of train set\n",
      "Processed 110_01.bmp in im_Koilocytotic of train set\n",
      "Processed 110_02.bmp in im_Koilocytotic of train set\n",
      "Processed 110_03.bmp in im_Koilocytotic of train set\n",
      "Processed 111_01.bmp in im_Koilocytotic of train set\n",
      "Processed 111_02.bmp in im_Koilocytotic of train set\n",
      "Processed 111_04.bmp in im_Koilocytotic of train set\n",
      "Processed 111_05.bmp in im_Koilocytotic of train set\n",
      "Processed 111_06.bmp in im_Koilocytotic of train set\n",
      "Processed 111_08.bmp in im_Koilocytotic of train set\n",
      "Processed 113_01.bmp in im_Koilocytotic of train set\n",
      "Processed 113_03.bmp in im_Koilocytotic of train set\n",
      "Processed 113_04.bmp in im_Koilocytotic of train set\n",
      "Processed 113_07.bmp in im_Koilocytotic of train set\n",
      "Processed 114_02.bmp in im_Koilocytotic of train set\n",
      "Processed 114_03.bmp in im_Koilocytotic of train set\n",
      "Processed 114_04.bmp in im_Koilocytotic of train set\n",
      "Processed 114_06.bmp in im_Koilocytotic of train set\n",
      "Processed 114_07.bmp in im_Koilocytotic of train set\n",
      "Processed 114_08.bmp in im_Koilocytotic of train set\n",
      "Processed 114_10.bmp in im_Koilocytotic of train set\n",
      "Processed 114_11.bmp in im_Koilocytotic of train set\n",
      "Processed 114_12.bmp in im_Koilocytotic of train set\n",
      "Processed 114_14.bmp in im_Koilocytotic of train set\n",
      "Processed 115_02.bmp in im_Koilocytotic of train set\n",
      "Processed 115_03.bmp in im_Koilocytotic of train set\n",
      "Processed 116_01.bmp in im_Koilocytotic of train set\n",
      "Processed 117_01.bmp in im_Koilocytotic of train set\n",
      "Processed 117_02.bmp in im_Koilocytotic of train set\n",
      "Processed 117_03.bmp in im_Koilocytotic of train set\n",
      "Processed 117_04.bmp in im_Koilocytotic of train set\n",
      "Processed 117_05.bmp in im_Koilocytotic of train set\n",
      "Processed 117_06.bmp in im_Koilocytotic of train set\n",
      "Processed 117_07.bmp in im_Koilocytotic of train set\n",
      "Processed 117_08.bmp in im_Koilocytotic of train set\n",
      "Processed 117_09.bmp in im_Koilocytotic of train set\n",
      "Processed 118_01.bmp in im_Koilocytotic of train set\n",
      "Processed 118_02.bmp in im_Koilocytotic of train set\n",
      "Processed 118_04.bmp in im_Koilocytotic of train set\n",
      "Processed 118_05.bmp in im_Koilocytotic of train set\n",
      "Processed 119_01.bmp in im_Koilocytotic of train set\n",
      "Processed 119_03.bmp in im_Koilocytotic of train set\n",
      "Processed 119_04.bmp in im_Koilocytotic of train set\n",
      "Processed 119_05.bmp in im_Koilocytotic of train set\n",
      "Processed 119_06.bmp in im_Koilocytotic of train set\n",
      "Processed 119_07.bmp in im_Koilocytotic of train set\n",
      "Processed 119_08.bmp in im_Koilocytotic of train set\n",
      "Processed 119_09.bmp in im_Koilocytotic of train set\n",
      "Processed 120_01.bmp in im_Koilocytotic of train set\n",
      "Processed 120_02.bmp in im_Koilocytotic of train set\n",
      "Processed 121_04.bmp in im_Koilocytotic of train set\n",
      "Processed 121_06.bmp in im_Koilocytotic of train set\n",
      "Processed 121_07.bmp in im_Koilocytotic of train set\n",
      "Processed 121_08.bmp in im_Koilocytotic of train set\n",
      "Processed 121_10.bmp in im_Koilocytotic of train set\n",
      "Processed 121_12.bmp in im_Koilocytotic of train set\n",
      "Processed 121_13.bmp in im_Koilocytotic of train set\n",
      "Processed 122_01.bmp in im_Koilocytotic of train set\n",
      "Processed 122_02.bmp in im_Koilocytotic of train set\n",
      "Processed 122_04.bmp in im_Koilocytotic of train set\n",
      "Processed 122_05.bmp in im_Koilocytotic of train set\n",
      "Processed 122_06.bmp in im_Koilocytotic of train set\n",
      "Processed 122_08.bmp in im_Koilocytotic of train set\n",
      "Processed 122_09.bmp in im_Koilocytotic of train set\n",
      "Processed 122_11.bmp in im_Koilocytotic of train set\n",
      "Processed 122_12.bmp in im_Koilocytotic of train set\n",
      "Processed 122_13.bmp in im_Koilocytotic of train set\n",
      "Processed 122_14.bmp in im_Koilocytotic of train set\n",
      "Processed 123_01.bmp in im_Koilocytotic of train set\n",
      "Processed 123_02.bmp in im_Koilocytotic of train set\n",
      "Processed 123_04.bmp in im_Koilocytotic of train set\n",
      "Processed 123_05.bmp in im_Koilocytotic of train set\n",
      "Processed 123_06.bmp in im_Koilocytotic of train set\n",
      "Processed 123_09.bmp in im_Koilocytotic of train set\n",
      "Processed 123_11.bmp in im_Koilocytotic of train set\n",
      "Processed 123_13.bmp in im_Koilocytotic of train set\n",
      "Processed 124_01.bmp in im_Koilocytotic of train set\n",
      "Processed 124_02.bmp in im_Koilocytotic of train set\n",
      "Processed 124_03.bmp in im_Koilocytotic of train set\n",
      "Processed 124_06.bmp in im_Koilocytotic of train set\n",
      "Processed 125_01.bmp in im_Koilocytotic of train set\n",
      "Processed 126_02.bmp in im_Koilocytotic of train set\n",
      "Processed 126_03.bmp in im_Koilocytotic of train set\n",
      "Processed 127_02.bmp in im_Koilocytotic of train set\n",
      "Processed 128_01.bmp in im_Koilocytotic of train set\n",
      "Processed 128_03.bmp in im_Koilocytotic of train set\n",
      "Processed 128_04.bmp in im_Koilocytotic of train set\n",
      "Processed 128_05.bmp in im_Koilocytotic of train set\n",
      "Processed 128_06.bmp in im_Koilocytotic of train set\n",
      "Processed 128_07.bmp in im_Koilocytotic of train set\n",
      "Processed 129_01.bmp in im_Koilocytotic of train set\n",
      "Processed 129_03.bmp in im_Koilocytotic of train set\n",
      "Processed 129_04.bmp in im_Koilocytotic of train set\n",
      "Processed 130_01.bmp in im_Koilocytotic of train set\n",
      "Processed 130_02.bmp in im_Koilocytotic of train set\n",
      "Processed 130_05.bmp in im_Koilocytotic of train set\n",
      "Processed 130_06.bmp in im_Koilocytotic of train set\n",
      "Processed 130_07.bmp in im_Koilocytotic of train set\n",
      "Processed 130_08.bmp in im_Koilocytotic of train set\n",
      "Processed 130_09.bmp in im_Koilocytotic of train set\n",
      "Processed 130_10.bmp in im_Koilocytotic of train set\n",
      "Processed 130_11.bmp in im_Koilocytotic of train set\n",
      "Processed 130_12.bmp in im_Koilocytotic of train set\n",
      "Processed 130_14.bmp in im_Koilocytotic of train set\n",
      "Processed 131_01.bmp in im_Koilocytotic of train set\n",
      "Processed 131_02.bmp in im_Koilocytotic of train set\n",
      "Processed 131_03.bmp in im_Koilocytotic of train set\n",
      "Processed 132_01.bmp in im_Koilocytotic of train set\n",
      "Processed 132_02.bmp in im_Koilocytotic of train set\n",
      "Processed 132_03.bmp in im_Koilocytotic of train set\n",
      "Processed 132_05.bmp in im_Koilocytotic of train set\n",
      "Processed 133_01.bmp in im_Koilocytotic of train set\n",
      "Processed 133_02.bmp in im_Koilocytotic of train set\n",
      "Processed 133_03.bmp in im_Koilocytotic of train set\n",
      "Processed 133_04.bmp in im_Koilocytotic of train set\n",
      "Processed 133_05.bmp in im_Koilocytotic of train set\n",
      "Processed 133_06.bmp in im_Koilocytotic of train set\n",
      "Processed 133_07.bmp in im_Koilocytotic of train set\n",
      "Processed 134_01.bmp in im_Koilocytotic of train set\n",
      "Processed 134_02.bmp in im_Koilocytotic of train set\n",
      "Processed 134_03.bmp in im_Koilocytotic of train set\n",
      "Processed 134_04.bmp in im_Koilocytotic of train set\n",
      "Processed 134_05.bmp in im_Koilocytotic of train set\n",
      "Processed 134_06.bmp in im_Koilocytotic of train set\n",
      "Processed 135_01.bmp in im_Koilocytotic of train set\n",
      "Processed 136_01.bmp in im_Koilocytotic of train set\n",
      "Processed 137_01.bmp in im_Koilocytotic of train set\n",
      "Processed 138_01.bmp in im_Koilocytotic of train set\n",
      "Processed 139_01.bmp in im_Koilocytotic of train set\n",
      "Processed 139_02.bmp in im_Koilocytotic of train set\n",
      "Processed 139_04.bmp in im_Koilocytotic of train set\n",
      "Processed 140_02.bmp in im_Koilocytotic of train set\n",
      "Processed 140_03.bmp in im_Koilocytotic of train set\n",
      "Processed 140_04.bmp in im_Koilocytotic of train set\n",
      "Processed 140_07.bmp in im_Koilocytotic of train set\n",
      "Processed 140_09.bmp in im_Koilocytotic of train set\n",
      "Processed 140_10.bmp in im_Koilocytotic of train set\n",
      "Processed 140_12.bmp in im_Koilocytotic of train set\n",
      "Processed 140_13.bmp in im_Koilocytotic of train set\n",
      "Processed 141_01.bmp in im_Koilocytotic of train set\n",
      "Processed 141_03.bmp in im_Koilocytotic of train set\n",
      "Processed 141_04.bmp in im_Koilocytotic of train set\n",
      "Processed 141_05.bmp in im_Koilocytotic of train set\n",
      "Processed 142_01.bmp in im_Koilocytotic of train set\n",
      "Processed 142_02.bmp in im_Koilocytotic of train set\n",
      "Processed 142_03.bmp in im_Koilocytotic of train set\n",
      "Processed 142_04.bmp in im_Koilocytotic of train set\n",
      "Processed 143_01.bmp in im_Koilocytotic of train set\n",
      "Processed 143_02.bmp in im_Koilocytotic of train set\n",
      "Processed 143_04.bmp in im_Koilocytotic of train set\n",
      "Processed 143_05.bmp in im_Koilocytotic of train set\n",
      "Processed 144_02.bmp in im_Koilocytotic of train set\n",
      "Processed 144_04.bmp in im_Koilocytotic of train set\n",
      "Processed 145_01.bmp in im_Koilocytotic of train set\n",
      "Processed 145_02.bmp in im_Koilocytotic of train set\n",
      "Processed 145_03.bmp in im_Koilocytotic of train set\n",
      "Processed 146_01.bmp in im_Koilocytotic of train set\n",
      "Processed 146_03.bmp in im_Koilocytotic of train set\n",
      "Processed 146_04.bmp in im_Koilocytotic of train set\n",
      "Processed 146_05.bmp in im_Koilocytotic of train set\n",
      "Processed 146_07.bmp in im_Koilocytotic of train set\n",
      "Processed 147_01.bmp in im_Koilocytotic of train set\n",
      "Processed 147_02.bmp in im_Koilocytotic of train set\n",
      "Processed 147_03.bmp in im_Koilocytotic of train set\n",
      "Processed 147_04.bmp in im_Koilocytotic of train set\n",
      "Processed 148_01.bmp in im_Koilocytotic of train set\n",
      "Processed 148_02.bmp in im_Koilocytotic of train set\n",
      "Processed 149_01.bmp in im_Koilocytotic of train set\n",
      "Processed 149_03.bmp in im_Koilocytotic of train set\n",
      "Processed 149_04.bmp in im_Koilocytotic of train set\n",
      "Processed 150_01.bmp in im_Koilocytotic of train set\n",
      "Processed 150_02.bmp in im_Koilocytotic of train set\n",
      "Processed 150_03.bmp in im_Koilocytotic of train set\n",
      "Processed 151_02.bmp in im_Koilocytotic of train set\n",
      "Processed 151_04.bmp in im_Koilocytotic of train set\n",
      "Processed 152_01.bmp in im_Koilocytotic of train set\n",
      "Processed 152_02.bmp in im_Koilocytotic of train set\n",
      "Processed 152_03.bmp in im_Koilocytotic of train set\n",
      "Processed 153_02.bmp in im_Koilocytotic of train set\n",
      "Processed 154_01.bmp in im_Koilocytotic of train set\n",
      "Processed 154_02.bmp in im_Koilocytotic of train set\n",
      "Processed 154_03.bmp in im_Koilocytotic of train set\n",
      "Processed 154_05.bmp in im_Koilocytotic of train set\n",
      "Processed 155_01.bmp in im_Koilocytotic of train set\n",
      "Processed 155_03.bmp in im_Koilocytotic of train set\n",
      "Processed 156_01.bmp in im_Koilocytotic of train set\n",
      "Processed 156_03.bmp in im_Koilocytotic of train set\n",
      "Processed 156_04.bmp in im_Koilocytotic of train set\n",
      "Processed 157_01.bmp in im_Koilocytotic of train set\n",
      "Processed 157_02.bmp in im_Koilocytotic of train set\n",
      "Processed 157_03.bmp in im_Koilocytotic of train set\n",
      "Processed 157_04.bmp in im_Koilocytotic of train set\n",
      "Processed 157_06.bmp in im_Koilocytotic of train set\n",
      "Processed 158_01.bmp in im_Koilocytotic of train set\n",
      "Processed 159_01.bmp in im_Koilocytotic of train set\n",
      "Processed 159_02.bmp in im_Koilocytotic of train set\n",
      "Processed 159_03.bmp in im_Koilocytotic of train set\n",
      "Processed 160_01.bmp in im_Koilocytotic of train set\n",
      "Processed 160_03.bmp in im_Koilocytotic of train set\n",
      "Processed 160_04.bmp in im_Koilocytotic of train set\n",
      "Processed 160_05.bmp in im_Koilocytotic of train set\n",
      "Processed 161_01.bmp in im_Koilocytotic of train set\n",
      "Processed 161_02.bmp in im_Koilocytotic of train set\n",
      "Processed 162_01.bmp in im_Koilocytotic of train set\n",
      "Processed 163_02.bmp in im_Koilocytotic of train set\n",
      "Processed 163_04.bmp in im_Koilocytotic of train set\n",
      "Processed 163_05.bmp in im_Koilocytotic of train set\n",
      "Processed 163_06.bmp in im_Koilocytotic of train set\n",
      "Processed 164_01.bmp in im_Koilocytotic of train set\n",
      "Processed 165_01.bmp in im_Koilocytotic of train set\n",
      "Processed 169_01.bmp in im_Koilocytotic of train set\n",
      "Processed 170_01.bmp in im_Koilocytotic of train set\n",
      "Processed 171_02.bmp in im_Koilocytotic of train set\n",
      "Processed 172_01.bmp in im_Koilocytotic of train set\n",
      "Processed 172_02.bmp in im_Koilocytotic of train set\n",
      "Processed 173_02.bmp in im_Koilocytotic of train set\n",
      "Processed 174_01.bmp in im_Koilocytotic of train set\n",
      "Processed 174_02.bmp in im_Koilocytotic of train set\n",
      "Processed 175_01.bmp in im_Koilocytotic of train set\n",
      "Processed 177_01.bmp in im_Koilocytotic of train set\n",
      "Processed 178_01.bmp in im_Koilocytotic of train set\n",
      "Processed 178_02.bmp in im_Koilocytotic of train set\n",
      "Processed 178_03.bmp in im_Koilocytotic of train set\n",
      "Processed 180_01.bmp in im_Koilocytotic of train set\n",
      "Processed 181_01.bmp in im_Koilocytotic of train set\n",
      "Processed 182_01.bmp in im_Koilocytotic of train set\n",
      "Processed 184_01.bmp in im_Koilocytotic of train set\n",
      "Processed 185_01.bmp in im_Koilocytotic of train set\n",
      "Processed 186_01.bmp in im_Koilocytotic of train set\n",
      "Processed 187_01.bmp in im_Koilocytotic of train set\n",
      "Processed 188_01.bmp in im_Koilocytotic of train set\n",
      "Processed 189_01.bmp in im_Koilocytotic of train set\n",
      "Processed 191_01.bmp in im_Koilocytotic of train set\n",
      "Processed 192_01.bmp in im_Koilocytotic of train set\n",
      "Processed 192_02.bmp in im_Koilocytotic of train set\n",
      "Processed 193_01.bmp in im_Koilocytotic of train set\n",
      "Processed 194_01.bmp in im_Koilocytotic of train set\n",
      "Processed 195_01.bmp in im_Koilocytotic of train set\n",
      "Processed 196_01.bmp in im_Koilocytotic of train set\n",
      "Processed 196_02.bmp in im_Koilocytotic of train set\n",
      "Processed 197_01.bmp in im_Koilocytotic of train set\n",
      "Processed 198_01.bmp in im_Koilocytotic of train set\n",
      "Processed 201_01.bmp in im_Koilocytotic of train set\n",
      "Processed 201_02.bmp in im_Koilocytotic of train set\n",
      "Processed 202_01.bmp in im_Koilocytotic of train set\n",
      "Processed 204_02.bmp in im_Koilocytotic of train set\n",
      "Processed 206_01.bmp in im_Koilocytotic of train set\n",
      "Processed 207_01.bmp in im_Koilocytotic of train set\n",
      "Processed 209_01.bmp in im_Koilocytotic of train set\n",
      "Processed 210_01.bmp in im_Koilocytotic of train set\n",
      "Processed 211_01.bmp in im_Koilocytotic of train set\n",
      "Processed 211_02.bmp in im_Koilocytotic of train set\n",
      "Processed 212_01.bmp in im_Koilocytotic of train set\n",
      "Processed 213_01.bmp in im_Koilocytotic of train set\n",
      "Processed 216_01.bmp in im_Koilocytotic of train set\n",
      "Processed 217_02.bmp in im_Koilocytotic of train set\n",
      "Processed 218_02.bmp in im_Koilocytotic of train set\n",
      "Processed 219_01.bmp in im_Koilocytotic of train set\n",
      "Processed 220_01.bmp in im_Koilocytotic of train set\n",
      "Processed 220_03.bmp in im_Koilocytotic of train set\n",
      "Processed 221_01.bmp in im_Koilocytotic of train set\n",
      "Processed 222_01.bmp in im_Koilocytotic of train set\n",
      "Processed 223_01.bmp in im_Koilocytotic of train set\n",
      "Processed 224_01.bmp in im_Koilocytotic of train set\n",
      "Processed 225_01.bmp in im_Koilocytotic of train set\n",
      "Processed 225_02.bmp in im_Koilocytotic of train set\n",
      "Processed 226_01.bmp in im_Koilocytotic of train set\n",
      "Processed 227_01.bmp in im_Koilocytotic of train set\n",
      "Processed 228_01.bmp in im_Koilocytotic of train set\n",
      "Processed 231_01.bmp in im_Koilocytotic of train set\n",
      "Processed 232_01.bmp in im_Koilocytotic of train set\n",
      "Processed 232_02.bmp in im_Koilocytotic of train set\n",
      "Processed 232_04.bmp in im_Koilocytotic of train set\n",
      "Processed 233_01.bmp in im_Koilocytotic of train set\n",
      "Processed 234_02.bmp in im_Koilocytotic of train set\n",
      "Processed 237_01.bmp in im_Koilocytotic of train set\n",
      "Processed 238_01.bmp in im_Koilocytotic of train set\n",
      "Processed 238_02.bmp in im_Koilocytotic of train set\n",
      "Processed 001_02.bmp in im_Metaplastic of train set\n",
      "Processed 001_03.bmp in im_Metaplastic of train set\n",
      "Processed 001_04.bmp in im_Metaplastic of train set\n",
      "Processed 001_05.bmp in im_Metaplastic of train set\n",
      "Processed 001_06.bmp in im_Metaplastic of train set\n",
      "Processed 001_07.bmp in im_Metaplastic of train set\n",
      "Processed 001_08.bmp in im_Metaplastic of train set\n",
      "Processed 001_09.bmp in im_Metaplastic of train set\n",
      "Processed 001_10.bmp in im_Metaplastic of train set\n",
      "Processed 001_12.bmp in im_Metaplastic of train set\n",
      "Processed 002_01.bmp in im_Metaplastic of train set\n",
      "Processed 002_02.bmp in im_Metaplastic of train set\n",
      "Processed 002_03.bmp in im_Metaplastic of train set\n",
      "Processed 002_04.bmp in im_Metaplastic of train set\n",
      "Processed 002_05.bmp in im_Metaplastic of train set\n",
      "Processed 002_07.bmp in im_Metaplastic of train set\n",
      "Processed 002_08.bmp in im_Metaplastic of train set\n",
      "Processed 002_10.bmp in im_Metaplastic of train set\n",
      "Processed 002_13.bmp in im_Metaplastic of train set\n",
      "Processed 002_14.bmp in im_Metaplastic of train set\n",
      "Processed 002_15.bmp in im_Metaplastic of train set\n",
      "Processed 003_01.bmp in im_Metaplastic of train set\n",
      "Processed 004_01.bmp in im_Metaplastic of train set\n",
      "Processed 006_02.bmp in im_Metaplastic of train set\n",
      "Processed 007_01.bmp in im_Metaplastic of train set\n",
      "Processed 007_02.bmp in im_Metaplastic of train set\n",
      "Processed 007_03.bmp in im_Metaplastic of train set\n",
      "Processed 009_02.bmp in im_Metaplastic of train set\n",
      "Processed 010_01.bmp in im_Metaplastic of train set\n",
      "Processed 010_02.bmp in im_Metaplastic of train set\n",
      "Processed 010_03.bmp in im_Metaplastic of train set\n",
      "Processed 011_02.bmp in im_Metaplastic of train set\n",
      "Processed 012_01.bmp in im_Metaplastic of train set\n",
      "Processed 013_03.bmp in im_Metaplastic of train set\n",
      "Processed 013_04.bmp in im_Metaplastic of train set\n",
      "Processed 014_01.bmp in im_Metaplastic of train set\n",
      "Processed 015_01.bmp in im_Metaplastic of train set\n",
      "Processed 015_02.bmp in im_Metaplastic of train set\n",
      "Processed 016_01.bmp in im_Metaplastic of train set\n",
      "Processed 016_02.bmp in im_Metaplastic of train set\n",
      "Processed 016_03.bmp in im_Metaplastic of train set\n",
      "Processed 016_04.bmp in im_Metaplastic of train set\n",
      "Processed 016_05.bmp in im_Metaplastic of train set\n",
      "Processed 017_02.bmp in im_Metaplastic of train set\n",
      "Processed 018_01.bmp in im_Metaplastic of train set\n",
      "Processed 018_04.bmp in im_Metaplastic of train set\n",
      "Processed 019_01.bmp in im_Metaplastic of train set\n",
      "Processed 019_02.bmp in im_Metaplastic of train set\n",
      "Processed 022_01.bmp in im_Metaplastic of train set\n",
      "Processed 023_01.bmp in im_Metaplastic of train set\n",
      "Processed 023_02.bmp in im_Metaplastic of train set\n",
      "Processed 023_06.bmp in im_Metaplastic of train set\n",
      "Processed 023_07.bmp in im_Metaplastic of train set\n",
      "Processed 023_08.bmp in im_Metaplastic of train set\n",
      "Processed 024_01.bmp in im_Metaplastic of train set\n",
      "Processed 024_04.bmp in im_Metaplastic of train set\n",
      "Processed 024_06.bmp in im_Metaplastic of train set\n",
      "Processed 024_07.bmp in im_Metaplastic of train set\n",
      "Processed 024_08.bmp in im_Metaplastic of train set\n",
      "Processed 025_01.bmp in im_Metaplastic of train set\n",
      "Processed 025_02.bmp in im_Metaplastic of train set\n",
      "Processed 025_03.bmp in im_Metaplastic of train set\n",
      "Processed 025_04.bmp in im_Metaplastic of train set\n",
      "Processed 025_05.bmp in im_Metaplastic of train set\n",
      "Processed 025_09.bmp in im_Metaplastic of train set\n",
      "Processed 025_11.bmp in im_Metaplastic of train set\n",
      "Processed 025_12.bmp in im_Metaplastic of train set\n",
      "Processed 025_13.bmp in im_Metaplastic of train set\n",
      "Processed 025_14.bmp in im_Metaplastic of train set\n",
      "Processed 026_01.bmp in im_Metaplastic of train set\n",
      "Processed 027_02.bmp in im_Metaplastic of train set\n",
      "Processed 027_03.bmp in im_Metaplastic of train set\n",
      "Processed 028_01.bmp in im_Metaplastic of train set\n",
      "Processed 028_02.bmp in im_Metaplastic of train set\n",
      "Processed 029_01.bmp in im_Metaplastic of train set\n",
      "Processed 030_01.bmp in im_Metaplastic of train set\n",
      "Processed 031_01.bmp in im_Metaplastic of train set\n",
      "Processed 032_01.bmp in im_Metaplastic of train set\n",
      "Processed 033_01.bmp in im_Metaplastic of train set\n",
      "Processed 033_02.bmp in im_Metaplastic of train set\n",
      "Processed 034_01.bmp in im_Metaplastic of train set\n",
      "Processed 036_01.bmp in im_Metaplastic of train set\n",
      "Processed 037_01.bmp in im_Metaplastic of train set\n",
      "Processed 037_02.bmp in im_Metaplastic of train set\n",
      "Processed 038_01.bmp in im_Metaplastic of train set\n",
      "Processed 039_01.bmp in im_Metaplastic of train set\n",
      "Processed 039_02.bmp in im_Metaplastic of train set\n",
      "Processed 041_01.bmp in im_Metaplastic of train set\n",
      "Processed 042_02.bmp in im_Metaplastic of train set\n",
      "Processed 045_01.bmp in im_Metaplastic of train set\n",
      "Processed 045_02.bmp in im_Metaplastic of train set\n",
      "Processed 046_02.bmp in im_Metaplastic of train set\n",
      "Processed 047_03.bmp in im_Metaplastic of train set\n",
      "Processed 048_01.bmp in im_Metaplastic of train set\n",
      "Processed 049_01.bmp in im_Metaplastic of train set\n",
      "Processed 050_01.bmp in im_Metaplastic of train set\n",
      "Processed 052_01.bmp in im_Metaplastic of train set\n",
      "Processed 052_02.bmp in im_Metaplastic of train set\n",
      "Processed 053_01.bmp in im_Metaplastic of train set\n",
      "Processed 053_02.bmp in im_Metaplastic of train set\n",
      "Processed 054_01.bmp in im_Metaplastic of train set\n",
      "Processed 055_01.bmp in im_Metaplastic of train set\n",
      "Processed 055_02.bmp in im_Metaplastic of train set\n",
      "Processed 057_01.bmp in im_Metaplastic of train set\n",
      "Processed 057_03.bmp in im_Metaplastic of train set\n",
      "Processed 060_01.bmp in im_Metaplastic of train set\n",
      "Processed 061_01.bmp in im_Metaplastic of train set\n",
      "Processed 063_01.bmp in im_Metaplastic of train set\n",
      "Processed 064_02.bmp in im_Metaplastic of train set\n",
      "Processed 065_01.bmp in im_Metaplastic of train set\n",
      "Processed 065_02.bmp in im_Metaplastic of train set\n",
      "Processed 067_01.bmp in im_Metaplastic of train set\n",
      "Processed 067_02.bmp in im_Metaplastic of train set\n",
      "Processed 067_03.bmp in im_Metaplastic of train set\n",
      "Processed 067_04.bmp in im_Metaplastic of train set\n",
      "Processed 068_01.bmp in im_Metaplastic of train set\n",
      "Processed 068_02.bmp in im_Metaplastic of train set\n",
      "Processed 068_03.bmp in im_Metaplastic of train set\n",
      "Processed 068_04.bmp in im_Metaplastic of train set\n",
      "Processed 068_06.bmp in im_Metaplastic of train set\n",
      "Processed 068_07.bmp in im_Metaplastic of train set\n",
      "Processed 068_08.bmp in im_Metaplastic of train set\n",
      "Processed 068_09.bmp in im_Metaplastic of train set\n",
      "Processed 068_10.bmp in im_Metaplastic of train set\n",
      "Processed 068_11.bmp in im_Metaplastic of train set\n",
      "Processed 069_02.bmp in im_Metaplastic of train set\n",
      "Processed 069_03.bmp in im_Metaplastic of train set\n",
      "Processed 069_04.bmp in im_Metaplastic of train set\n",
      "Processed 069_05.bmp in im_Metaplastic of train set\n",
      "Processed 069_06.bmp in im_Metaplastic of train set\n",
      "Processed 069_07.bmp in im_Metaplastic of train set\n",
      "Processed 070_01.bmp in im_Metaplastic of train set\n",
      "Processed 071_01.bmp in im_Metaplastic of train set\n",
      "Processed 071_02.bmp in im_Metaplastic of train set\n",
      "Processed 072_02.bmp in im_Metaplastic of train set\n",
      "Processed 072_06.bmp in im_Metaplastic of train set\n",
      "Processed 072_07.bmp in im_Metaplastic of train set\n",
      "Processed 072_08.bmp in im_Metaplastic of train set\n",
      "Processed 072_10.bmp in im_Metaplastic of train set\n",
      "Processed 072_11.bmp in im_Metaplastic of train set\n",
      "Processed 072_12.bmp in im_Metaplastic of train set\n",
      "Processed 072_14.bmp in im_Metaplastic of train set\n",
      "Processed 073_02.bmp in im_Metaplastic of train set\n",
      "Processed 073_03.bmp in im_Metaplastic of train set\n",
      "Processed 073_04.bmp in im_Metaplastic of train set\n",
      "Processed 073_06.bmp in im_Metaplastic of train set\n",
      "Processed 073_07.bmp in im_Metaplastic of train set\n",
      "Processed 074_01.bmp in im_Metaplastic of train set\n",
      "Processed 074_03.bmp in im_Metaplastic of train set\n",
      "Processed 074_04.bmp in im_Metaplastic of train set\n",
      "Processed 074_07.bmp in im_Metaplastic of train set\n",
      "Processed 074_08.bmp in im_Metaplastic of train set\n",
      "Processed 074_09.bmp in im_Metaplastic of train set\n",
      "Processed 074_10.bmp in im_Metaplastic of train set\n",
      "Processed 074_11.bmp in im_Metaplastic of train set\n",
      "Processed 075_01.bmp in im_Metaplastic of train set\n",
      "Processed 076_01.bmp in im_Metaplastic of train set\n",
      "Processed 076_02.bmp in im_Metaplastic of train set\n",
      "Processed 077_01.bmp in im_Metaplastic of train set\n",
      "Processed 077_02.bmp in im_Metaplastic of train set\n",
      "Processed 077_03.bmp in im_Metaplastic of train set\n",
      "Processed 077_05.bmp in im_Metaplastic of train set\n",
      "Processed 077_06.bmp in im_Metaplastic of train set\n",
      "Processed 077_07.bmp in im_Metaplastic of train set\n",
      "Processed 077_09.bmp in im_Metaplastic of train set\n",
      "Processed 077_10.bmp in im_Metaplastic of train set\n",
      "Processed 078_01.bmp in im_Metaplastic of train set\n",
      "Processed 078_02.bmp in im_Metaplastic of train set\n",
      "Processed 078_03.bmp in im_Metaplastic of train set\n",
      "Processed 079_01.bmp in im_Metaplastic of train set\n",
      "Processed 079_02.bmp in im_Metaplastic of train set\n",
      "Processed 079_04.bmp in im_Metaplastic of train set\n",
      "Processed 080_03.bmp in im_Metaplastic of train set\n",
      "Processed 080_04.bmp in im_Metaplastic of train set\n",
      "Processed 080_05.bmp in im_Metaplastic of train set\n",
      "Processed 081_01.bmp in im_Metaplastic of train set\n",
      "Processed 081_02.bmp in im_Metaplastic of train set\n",
      "Processed 081_03.bmp in im_Metaplastic of train set\n",
      "Processed 081_04.bmp in im_Metaplastic of train set\n",
      "Processed 081_07.bmp in im_Metaplastic of train set\n",
      "Processed 081_08.bmp in im_Metaplastic of train set\n",
      "Processed 082_01.bmp in im_Metaplastic of train set\n",
      "Processed 082_03.bmp in im_Metaplastic of train set\n",
      "Processed 082_04.bmp in im_Metaplastic of train set\n",
      "Processed 082_05.bmp in im_Metaplastic of train set\n",
      "Processed 082_06.bmp in im_Metaplastic of train set\n",
      "Processed 082_07.bmp in im_Metaplastic of train set\n",
      "Processed 082_08.bmp in im_Metaplastic of train set\n",
      "Processed 082_09.bmp in im_Metaplastic of train set\n",
      "Processed 082_10.bmp in im_Metaplastic of train set\n",
      "Processed 083_01.bmp in im_Metaplastic of train set\n",
      "Processed 083_04.bmp in im_Metaplastic of train set\n",
      "Processed 083_06.bmp in im_Metaplastic of train set\n",
      "Processed 083_07.bmp in im_Metaplastic of train set\n",
      "Processed 084_01.bmp in im_Metaplastic of train set\n",
      "Processed 085_01.bmp in im_Metaplastic of train set\n",
      "Processed 085_02.bmp in im_Metaplastic of train set\n",
      "Processed 085_03.bmp in im_Metaplastic of train set\n",
      "Processed 086_01.bmp in im_Metaplastic of train set\n",
      "Processed 086_02.bmp in im_Metaplastic of train set\n",
      "Processed 087_01.bmp in im_Metaplastic of train set\n",
      "Processed 088_01.bmp in im_Metaplastic of train set\n",
      "Processed 088_02.bmp in im_Metaplastic of train set\n",
      "Processed 088_03.bmp in im_Metaplastic of train set\n",
      "Processed 089_01.bmp in im_Metaplastic of train set\n",
      "Processed 090_01.bmp in im_Metaplastic of train set\n",
      "Processed 090_02.bmp in im_Metaplastic of train set\n",
      "Processed 090_03.bmp in im_Metaplastic of train set\n",
      "Processed 091_01.bmp in im_Metaplastic of train set\n",
      "Processed 091_02.bmp in im_Metaplastic of train set\n",
      "Processed 091_04.bmp in im_Metaplastic of train set\n",
      "Processed 092_01.bmp in im_Metaplastic of train set\n",
      "Processed 092_02.bmp in im_Metaplastic of train set\n",
      "Processed 092_03.bmp in im_Metaplastic of train set\n",
      "Processed 092_04.bmp in im_Metaplastic of train set\n",
      "Processed 092_05.bmp in im_Metaplastic of train set\n",
      "Processed 092_06.bmp in im_Metaplastic of train set\n",
      "Processed 092_07.bmp in im_Metaplastic of train set\n",
      "Processed 092_08.bmp in im_Metaplastic of train set\n",
      "Processed 093_01.bmp in im_Metaplastic of train set\n",
      "Processed 094_01.bmp in im_Metaplastic of train set\n",
      "Processed 094_02.bmp in im_Metaplastic of train set\n",
      "Processed 094_04.bmp in im_Metaplastic of train set\n",
      "Processed 095_01.bmp in im_Metaplastic of train set\n",
      "Processed 095_02.bmp in im_Metaplastic of train set\n",
      "Processed 095_03.bmp in im_Metaplastic of train set\n",
      "Processed 096_01.bmp in im_Metaplastic of train set\n",
      "Processed 096_05.bmp in im_Metaplastic of train set\n",
      "Processed 097_01.bmp in im_Metaplastic of train set\n",
      "Processed 097_02.bmp in im_Metaplastic of train set\n",
      "Processed 097_03.bmp in im_Metaplastic of train set\n",
      "Processed 097_04.bmp in im_Metaplastic of train set\n",
      "Processed 097_05.bmp in im_Metaplastic of train set\n",
      "Processed 098_01.bmp in im_Metaplastic of train set\n",
      "Processed 100_01.bmp in im_Metaplastic of train set\n",
      "Processed 101_01.bmp in im_Metaplastic of train set\n",
      "Processed 101_03.bmp in im_Metaplastic of train set\n",
      "Processed 101_04.bmp in im_Metaplastic of train set\n",
      "Processed 102_01.bmp in im_Metaplastic of train set\n",
      "Processed 102_03.bmp in im_Metaplastic of train set\n",
      "Processed 102_05.bmp in im_Metaplastic of train set\n",
      "Processed 102_06.bmp in im_Metaplastic of train set\n",
      "Processed 102_07.bmp in im_Metaplastic of train set\n",
      "Processed 102_08.bmp in im_Metaplastic of train set\n",
      "Processed 103_01.bmp in im_Metaplastic of train set\n",
      "Processed 105_01.bmp in im_Metaplastic of train set\n",
      "Processed 106_01.bmp in im_Metaplastic of train set\n",
      "Processed 107_01.bmp in im_Metaplastic of train set\n",
      "Processed 107_02.bmp in im_Metaplastic of train set\n",
      "Processed 108_01.bmp in im_Metaplastic of train set\n",
      "Processed 111_01.bmp in im_Metaplastic of train set\n",
      "Processed 112_01.bmp in im_Metaplastic of train set\n",
      "Processed 115_01.bmp in im_Metaplastic of train set\n",
      "Processed 116_01.bmp in im_Metaplastic of train set\n",
      "Processed 116_03.bmp in im_Metaplastic of train set\n",
      "Processed 116_04.bmp in im_Metaplastic of train set\n",
      "Processed 116_05.bmp in im_Metaplastic of train set\n",
      "Processed 117_01.bmp in im_Metaplastic of train set\n",
      "Processed 118_01.bmp in im_Metaplastic of train set\n",
      "Processed 118_02.bmp in im_Metaplastic of train set\n",
      "Processed 118_03.bmp in im_Metaplastic of train set\n",
      "Processed 118_04.bmp in im_Metaplastic of train set\n",
      "Processed 119_01.bmp in im_Metaplastic of train set\n",
      "Processed 119_02.bmp in im_Metaplastic of train set\n",
      "Processed 120_01.bmp in im_Metaplastic of train set\n",
      "Processed 121_01.bmp in im_Metaplastic of train set\n",
      "Processed 122_02.bmp in im_Metaplastic of train set\n",
      "Processed 123_02.bmp in im_Metaplastic of train set\n",
      "Processed 124_01.bmp in im_Metaplastic of train set\n",
      "Processed 124_02.bmp in im_Metaplastic of train set\n",
      "Processed 124_03.bmp in im_Metaplastic of train set\n",
      "Processed 124_04.bmp in im_Metaplastic of train set\n",
      "Processed 126_02.bmp in im_Metaplastic of train set\n",
      "Processed 127_01.bmp in im_Metaplastic of train set\n",
      "Processed 128_01.bmp in im_Metaplastic of train set\n",
      "Processed 129_01.bmp in im_Metaplastic of train set\n",
      "Processed 129_02.bmp in im_Metaplastic of train set\n",
      "Processed 129_03.bmp in im_Metaplastic of train set\n",
      "Processed 130_01.bmp in im_Metaplastic of train set\n",
      "Processed 131_01.bmp in im_Metaplastic of train set\n",
      "Processed 132_01.bmp in im_Metaplastic of train set\n",
      "Processed 132_02.bmp in im_Metaplastic of train set\n",
      "Processed 132_03.bmp in im_Metaplastic of train set\n",
      "Processed 132_04.bmp in im_Metaplastic of train set\n",
      "Processed 132_05.bmp in im_Metaplastic of train set\n",
      "Processed 134_01.bmp in im_Metaplastic of train set\n",
      "Processed 135_01.bmp in im_Metaplastic of train set\n",
      "Processed 136_01.bmp in im_Metaplastic of train set\n",
      "Processed 137_01.bmp in im_Metaplastic of train set\n",
      "Processed 138_01.bmp in im_Metaplastic of train set\n",
      "Processed 138_02.bmp in im_Metaplastic of train set\n",
      "Processed 138_03.bmp in im_Metaplastic of train set\n",
      "Processed 138_04.bmp in im_Metaplastic of train set\n",
      "Processed 138_05.bmp in im_Metaplastic of train set\n",
      "Processed 139_01.bmp in im_Metaplastic of train set\n",
      "Processed 139_02.bmp in im_Metaplastic of train set\n",
      "Processed 139_03.bmp in im_Metaplastic of train set\n",
      "Processed 139_04.bmp in im_Metaplastic of train set\n",
      "Processed 139_05.bmp in im_Metaplastic of train set\n",
      "Processed 139_06.bmp in im_Metaplastic of train set\n",
      "Processed 140_01.bmp in im_Metaplastic of train set\n",
      "Processed 140_03.bmp in im_Metaplastic of train set\n",
      "Processed 140_04.bmp in im_Metaplastic of train set\n",
      "Processed 141_01.bmp in im_Metaplastic of train set\n",
      "Processed 141_02.bmp in im_Metaplastic of train set\n",
      "Processed 141_03.bmp in im_Metaplastic of train set\n",
      "Processed 141_04.bmp in im_Metaplastic of train set\n",
      "Processed 141_06.bmp in im_Metaplastic of train set\n",
      "Processed 141_08.bmp in im_Metaplastic of train set\n",
      "Processed 143_01.bmp in im_Metaplastic of train set\n",
      "Processed 144_01.bmp in im_Metaplastic of train set\n",
      "Processed 144_02.bmp in im_Metaplastic of train set\n",
      "Processed 144_03.bmp in im_Metaplastic of train set\n",
      "Processed 145_01.bmp in im_Metaplastic of train set\n",
      "Processed 145_02.bmp in im_Metaplastic of train set\n",
      "Processed 145_03.bmp in im_Metaplastic of train set\n",
      "Processed 146_01.bmp in im_Metaplastic of train set\n",
      "Processed 146_02.bmp in im_Metaplastic of train set\n",
      "Processed 146_04.bmp in im_Metaplastic of train set\n",
      "Processed 147_02.bmp in im_Metaplastic of train set\n",
      "Processed 148_01.bmp in im_Metaplastic of train set\n",
      "Processed 148_02.bmp in im_Metaplastic of train set\n",
      "Processed 148_03.bmp in im_Metaplastic of train set\n",
      "Processed 148_05.bmp in im_Metaplastic of train set\n",
      "Processed 148_06.bmp in im_Metaplastic of train set\n",
      "Processed 148_08.bmp in im_Metaplastic of train set\n",
      "Processed 148_09.bmp in im_Metaplastic of train set\n",
      "Processed 151_01.bmp in im_Metaplastic of train set\n",
      "Processed 151_02.bmp in im_Metaplastic of train set\n",
      "Processed 151_03.bmp in im_Metaplastic of train set\n",
      "Processed 152_01.bmp in im_Metaplastic of train set\n",
      "Processed 152_02.bmp in im_Metaplastic of train set\n",
      "Processed 153_01.bmp in im_Metaplastic of train set\n",
      "Processed 154_01.bmp in im_Metaplastic of train set\n",
      "Processed 155_01.bmp in im_Metaplastic of train set\n",
      "Processed 155_02.bmp in im_Metaplastic of train set\n",
      "Processed 156_01.bmp in im_Metaplastic of train set\n",
      "Processed 156_02.bmp in im_Metaplastic of train set\n",
      "Processed 158_01.bmp in im_Metaplastic of train set\n",
      "Processed 159_01.bmp in im_Metaplastic of train set\n",
      "Processed 159_02.bmp in im_Metaplastic of train set\n",
      "Processed 159_03.bmp in im_Metaplastic of train set\n",
      "Processed 160_01.bmp in im_Metaplastic of train set\n",
      "Processed 161_01.bmp in im_Metaplastic of train set\n",
      "Processed 163_01.bmp in im_Metaplastic of train set\n",
      "Processed 164_04.bmp in im_Metaplastic of train set\n",
      "Processed 164_06.bmp in im_Metaplastic of train set\n",
      "Processed 164_07.bmp in im_Metaplastic of train set\n",
      "Processed 166_01.bmp in im_Metaplastic of train set\n",
      "Processed 166_02.bmp in im_Metaplastic of train set\n",
      "Processed 166_03.bmp in im_Metaplastic of train set\n",
      "Processed 167_01.bmp in im_Metaplastic of train set\n",
      "Processed 167_03.bmp in im_Metaplastic of train set\n",
      "Processed 167_05.bmp in im_Metaplastic of train set\n",
      "Processed 167_06.bmp in im_Metaplastic of train set\n",
      "Processed 169_01.bmp in im_Metaplastic of train set\n",
      "Processed 169_02.bmp in im_Metaplastic of train set\n",
      "Processed 170_01.bmp in im_Metaplastic of train set\n",
      "Processed 170_02.bmp in im_Metaplastic of train set\n",
      "Processed 170_03.bmp in im_Metaplastic of train set\n",
      "Processed 170_04.bmp in im_Metaplastic of train set\n",
      "Processed 170_05.bmp in im_Metaplastic of train set\n",
      "Processed 171_01.bmp in im_Metaplastic of train set\n",
      "Processed 171_04.bmp in im_Metaplastic of train set\n",
      "Processed 172_01.bmp in im_Metaplastic of train set\n",
      "Processed 173_01.bmp in im_Metaplastic of train set\n",
      "Processed 174_02.bmp in im_Metaplastic of train set\n",
      "Processed 175_01.bmp in im_Metaplastic of train set\n",
      "Processed 175_02.bmp in im_Metaplastic of train set\n",
      "Processed 176_02.bmp in im_Metaplastic of train set\n",
      "Processed 180_01.bmp in im_Metaplastic of train set\n",
      "Processed 180_02.bmp in im_Metaplastic of train set\n",
      "Processed 181_01.bmp in im_Metaplastic of train set\n",
      "Processed 183_01.bmp in im_Metaplastic of train set\n",
      "Processed 185_03.bmp in im_Metaplastic of train set\n",
      "Processed 185_04.bmp in im_Metaplastic of train set\n",
      "Processed 186_01.bmp in im_Metaplastic of train set\n",
      "Processed 187_01.bmp in im_Metaplastic of train set\n",
      "Processed 188_02.bmp in im_Metaplastic of train set\n",
      "Processed 189_02.bmp in im_Metaplastic of train set\n",
      "Processed 191_01.bmp in im_Metaplastic of train set\n",
      "Processed 192_01.bmp in im_Metaplastic of train set\n",
      "Processed 193_01.bmp in im_Metaplastic of train set\n",
      "Processed 193_02.bmp in im_Metaplastic of train set\n",
      "Processed 193_03.bmp in im_Metaplastic of train set\n",
      "Processed 193_04.bmp in im_Metaplastic of train set\n",
      "Processed 193_05.bmp in im_Metaplastic of train set\n",
      "Processed 193_07.bmp in im_Metaplastic of train set\n",
      "Processed 194_01.bmp in im_Metaplastic of train set\n",
      "Processed 194_02.bmp in im_Metaplastic of train set\n",
      "Processed 195_01.bmp in im_Metaplastic of train set\n",
      "Processed 196_01.bmp in im_Metaplastic of train set\n",
      "Processed 197_01.bmp in im_Metaplastic of train set\n",
      "Processed 197_02.bmp in im_Metaplastic of train set\n",
      "Processed 197_03.bmp in im_Metaplastic of train set\n",
      "Processed 197_04.bmp in im_Metaplastic of train set\n",
      "Processed 198_01.bmp in im_Metaplastic of train set\n",
      "Processed 198_02.bmp in im_Metaplastic of train set\n",
      "Processed 198_03.bmp in im_Metaplastic of train set\n",
      "Processed 198_04.bmp in im_Metaplastic of train set\n",
      "Processed 199_01.bmp in im_Metaplastic of train set\n",
      "Processed 199_02.bmp in im_Metaplastic of train set\n",
      "Processed 199_03.bmp in im_Metaplastic of train set\n",
      "Processed 200_01.bmp in im_Metaplastic of train set\n",
      "Processed 200_02.bmp in im_Metaplastic of train set\n",
      "Processed 200_03.bmp in im_Metaplastic of train set\n",
      "Processed 201_03.bmp in im_Metaplastic of train set\n",
      "Processed 201_04.bmp in im_Metaplastic of train set\n",
      "Processed 201_05.bmp in im_Metaplastic of train set\n",
      "Processed 201_06.bmp in im_Metaplastic of train set\n",
      "Processed 201_07.bmp in im_Metaplastic of train set\n",
      "Processed 201_08.bmp in im_Metaplastic of train set\n",
      "Processed 202_01.bmp in im_Metaplastic of train set\n",
      "Processed 202_03.bmp in im_Metaplastic of train set\n",
      "Processed 202_04.bmp in im_Metaplastic of train set\n",
      "Processed 202_06.bmp in im_Metaplastic of train set\n",
      "Processed 202_07.bmp in im_Metaplastic of train set\n",
      "Processed 202_08.bmp in im_Metaplastic of train set\n",
      "Processed 202_09.bmp in im_Metaplastic of train set\n",
      "Processed 203_01.bmp in im_Metaplastic of train set\n",
      "Processed 203_02.bmp in im_Metaplastic of train set\n",
      "Processed 203_04.bmp in im_Metaplastic of train set\n",
      "Processed 204_01.bmp in im_Metaplastic of train set\n",
      "Processed 204_04.bmp in im_Metaplastic of train set\n",
      "Processed 206_01.bmp in im_Metaplastic of train set\n",
      "Processed 206_02.bmp in im_Metaplastic of train set\n",
      "Processed 207_03.bmp in im_Metaplastic of train set\n",
      "Processed 208_01.bmp in im_Metaplastic of train set\n",
      "Processed 208_02.bmp in im_Metaplastic of train set\n",
      "Processed 208_03.bmp in im_Metaplastic of train set\n",
      "Processed 209_01.bmp in im_Metaplastic of train set\n",
      "Processed 209_03.bmp in im_Metaplastic of train set\n",
      "Processed 210_02.bmp in im_Metaplastic of train set\n",
      "Processed 210_03.bmp in im_Metaplastic of train set\n",
      "Processed 210_04.bmp in im_Metaplastic of train set\n",
      "Processed 212_01.bmp in im_Metaplastic of train set\n",
      "Processed 212_02.bmp in im_Metaplastic of train set\n",
      "Processed 212_03.bmp in im_Metaplastic of train set\n",
      "Processed 212_04.bmp in im_Metaplastic of train set\n",
      "Processed 214_01.bmp in im_Metaplastic of train set\n",
      "Processed 215_01.bmp in im_Metaplastic of train set\n",
      "Processed 217_01.bmp in im_Metaplastic of train set\n",
      "Processed 217_02.bmp in im_Metaplastic of train set\n",
      "Processed 217_03.bmp in im_Metaplastic of train set\n",
      "Processed 217_04.bmp in im_Metaplastic of train set\n",
      "Processed 217_06.bmp in im_Metaplastic of train set\n",
      "Processed 217_09.bmp in im_Metaplastic of train set\n",
      "Processed 218_02.bmp in im_Metaplastic of train set\n",
      "Processed 218_04.bmp in im_Metaplastic of train set\n",
      "Processed 218_05.bmp in im_Metaplastic of train set\n",
      "Processed 218_06.bmp in im_Metaplastic of train set\n",
      "Processed 220_01.bmp in im_Metaplastic of train set\n",
      "Processed 221_01.bmp in im_Metaplastic of train set\n",
      "Processed 221_02.bmp in im_Metaplastic of train set\n",
      "Processed 221_03.bmp in im_Metaplastic of train set\n",
      "Processed 222_01.bmp in im_Metaplastic of train set\n",
      "Processed 223_01.bmp in im_Metaplastic of train set\n",
      "Processed 223_02.bmp in im_Metaplastic of train set\n",
      "Processed 223_03.bmp in im_Metaplastic of train set\n",
      "Processed 224_02.bmp in im_Metaplastic of train set\n",
      "Processed 225_01.bmp in im_Metaplastic of train set\n",
      "Processed 225_02.bmp in im_Metaplastic of train set\n",
      "Processed 225_05.bmp in im_Metaplastic of train set\n",
      "Processed 226_01.bmp in im_Metaplastic of train set\n",
      "Processed 227_01.bmp in im_Metaplastic of train set\n",
      "Processed 227_03.bmp in im_Metaplastic of train set\n",
      "Processed 227_04.bmp in im_Metaplastic of train set\n",
      "Processed 227_05.bmp in im_Metaplastic of train set\n",
      "Processed 228_03.bmp in im_Metaplastic of train set\n",
      "Processed 229_01.bmp in im_Metaplastic of train set\n",
      "Processed 230_02.bmp in im_Metaplastic of train set\n",
      "Processed 230_03.bmp in im_Metaplastic of train set\n",
      "Processed 231_01.bmp in im_Metaplastic of train set\n",
      "Processed 231_02.bmp in im_Metaplastic of train set\n",
      "Processed 232_01.bmp in im_Metaplastic of train set\n",
      "Processed 233_01.bmp in im_Metaplastic of train set\n",
      "Processed 234_01.bmp in im_Metaplastic of train set\n",
      "Processed 234_02.bmp in im_Metaplastic of train set\n",
      "Processed 235_01.bmp in im_Metaplastic of train set\n",
      "Processed 236_01.bmp in im_Metaplastic of train set\n",
      "Processed 237_01.bmp in im_Metaplastic of train set\n",
      "Processed 239_01.bmp in im_Metaplastic of train set\n",
      "Processed 239_04.bmp in im_Metaplastic of train set\n",
      "Processed 239_05.bmp in im_Metaplastic of train set\n",
      "Processed 240_01.bmp in im_Metaplastic of train set\n",
      "Processed 242_01.bmp in im_Metaplastic of train set\n",
      "Processed 242_02.bmp in im_Metaplastic of train set\n",
      "Processed 243_01.bmp in im_Metaplastic of train set\n",
      "Processed 243_02.bmp in im_Metaplastic of train set\n",
      "Processed 244_01.bmp in im_Metaplastic of train set\n",
      "Processed 244_02.bmp in im_Metaplastic of train set\n",
      "Processed 245_01.bmp in im_Metaplastic of train set\n",
      "Processed 245_02.bmp in im_Metaplastic of train set\n",
      "Processed 245_03.bmp in im_Metaplastic of train set\n",
      "Processed 246_01.bmp in im_Metaplastic of train set\n",
      "Processed 247_01.bmp in im_Metaplastic of train set\n",
      "Processed 248_01.bmp in im_Metaplastic of train set\n",
      "Processed 249_01.bmp in im_Metaplastic of train set\n",
      "Processed 249_02.bmp in im_Metaplastic of train set\n",
      "Processed 251_01.bmp in im_Metaplastic of train set\n",
      "Processed 251_02.bmp in im_Metaplastic of train set\n",
      "Processed 252_01.bmp in im_Metaplastic of train set\n",
      "Processed 252_02.bmp in im_Metaplastic of train set\n",
      "Processed 253_01.bmp in im_Metaplastic of train set\n",
      "Processed 253_02.bmp in im_Metaplastic of train set\n",
      "Processed 253_03.bmp in im_Metaplastic of train set\n",
      "Processed 253_04.bmp in im_Metaplastic of train set\n",
      "Processed 253_05.bmp in im_Metaplastic of train set\n",
      "Processed 253_06.bmp in im_Metaplastic of train set\n",
      "Processed 253_07.bmp in im_Metaplastic of train set\n",
      "Processed 254_01.bmp in im_Metaplastic of train set\n",
      "Processed 254_02.bmp in im_Metaplastic of train set\n",
      "Processed 255_01.bmp in im_Metaplastic of train set\n",
      "Processed 255_02.bmp in im_Metaplastic of train set\n",
      "Processed 256_02.bmp in im_Metaplastic of train set\n",
      "Processed 256_04.bmp in im_Metaplastic of train set\n",
      "Processed 257_01.bmp in im_Metaplastic of train set\n",
      "Processed 257_02.bmp in im_Metaplastic of train set\n",
      "Processed 258_02.bmp in im_Metaplastic of train set\n",
      "Processed 260_01.bmp in im_Metaplastic of train set\n",
      "Processed 260_02.bmp in im_Metaplastic of train set\n",
      "Processed 261_01.bmp in im_Metaplastic of train set\n",
      "Processed 261_04.bmp in im_Metaplastic of train set\n",
      "Processed 261_06.bmp in im_Metaplastic of train set\n",
      "Processed 261_07.bmp in im_Metaplastic of train set\n",
      "Processed 261_08.bmp in im_Metaplastic of train set\n",
      "Processed 261_09.bmp in im_Metaplastic of train set\n",
      "Processed 261_11.bmp in im_Metaplastic of train set\n",
      "Processed 262_03.bmp in im_Metaplastic of train set\n",
      "Processed 262_04.bmp in im_Metaplastic of train set\n",
      "Processed 262_05.bmp in im_Metaplastic of train set\n",
      "Processed 263_01.bmp in im_Metaplastic of train set\n",
      "Processed 263_02.bmp in im_Metaplastic of train set\n",
      "Processed 264_01.bmp in im_Metaplastic of train set\n",
      "Processed 264_02.bmp in im_Metaplastic of train set\n",
      "Processed 264_03.bmp in im_Metaplastic of train set\n",
      "Processed 264_04.bmp in im_Metaplastic of train set\n",
      "Processed 264_05.bmp in im_Metaplastic of train set\n",
      "Processed 264_06.bmp in im_Metaplastic of train set\n",
      "Processed 264_07.bmp in im_Metaplastic of train set\n",
      "Processed 264_08.bmp in im_Metaplastic of train set\n",
      "Processed 264_09.bmp in im_Metaplastic of train set\n",
      "Processed 265_01.bmp in im_Metaplastic of train set\n",
      "Processed 265_02.bmp in im_Metaplastic of train set\n",
      "Processed 267_01.bmp in im_Metaplastic of train set\n",
      "Processed 267_02.bmp in im_Metaplastic of train set\n",
      "Processed 268_02.bmp in im_Metaplastic of train set\n",
      "Processed 268_03.bmp in im_Metaplastic of train set\n",
      "Processed 268_05.bmp in im_Metaplastic of train set\n",
      "Processed 269_01.bmp in im_Metaplastic of train set\n",
      "Processed 269_03.bmp in im_Metaplastic of train set\n",
      "Processed 270_01.bmp in im_Metaplastic of train set\n",
      "Processed 271_02.bmp in im_Metaplastic of train set\n",
      "Processed 271_03.bmp in im_Metaplastic of train set\n",
      "Processed 271_04.bmp in im_Metaplastic of train set\n",
      "Processed 001_01.bmp in im_Parabasal of train set\n",
      "Processed 001_03.bmp in im_Parabasal of train set\n",
      "Processed 001_04.bmp in im_Parabasal of train set\n",
      "Processed 001_05.bmp in im_Parabasal of train set\n",
      "Processed 001_06.bmp in im_Parabasal of train set\n",
      "Processed 001_07.bmp in im_Parabasal of train set\n",
      "Processed 001_08.bmp in im_Parabasal of train set\n",
      "Processed 001_09.bmp in im_Parabasal of train set\n",
      "Processed 001_10.bmp in im_Parabasal of train set\n",
      "Processed 001_12.bmp in im_Parabasal of train set\n",
      "Processed 001_13.bmp in im_Parabasal of train set\n",
      "Processed 002_01.bmp in im_Parabasal of train set\n",
      "Processed 002_02.bmp in im_Parabasal of train set\n",
      "Processed 002_03.bmp in im_Parabasal of train set\n",
      "Processed 002_04.bmp in im_Parabasal of train set\n",
      "Processed 003_01.bmp in im_Parabasal of train set\n",
      "Processed 003_02.bmp in im_Parabasal of train set\n",
      "Processed 003_03.bmp in im_Parabasal of train set\n",
      "Processed 003_06.bmp in im_Parabasal of train set\n",
      "Processed 003_10.bmp in im_Parabasal of train set\n",
      "Processed 003_11.bmp in im_Parabasal of train set\n",
      "Processed 004_05.bmp in im_Parabasal of train set\n",
      "Processed 004_06.bmp in im_Parabasal of train set\n",
      "Processed 004_07.bmp in im_Parabasal of train set\n",
      "Processed 004_08.bmp in im_Parabasal of train set\n",
      "Processed 004_09.bmp in im_Parabasal of train set\n",
      "Processed 004_11.bmp in im_Parabasal of train set\n",
      "Processed 005_01.bmp in im_Parabasal of train set\n",
      "Processed 005_02.bmp in im_Parabasal of train set\n",
      "Processed 005_03.bmp in im_Parabasal of train set\n",
      "Processed 005_04.bmp in im_Parabasal of train set\n",
      "Processed 005_05.bmp in im_Parabasal of train set\n",
      "Processed 005_06.bmp in im_Parabasal of train set\n",
      "Processed 006_02.bmp in im_Parabasal of train set\n",
      "Processed 006_03.bmp in im_Parabasal of train set\n",
      "Processed 006_04.bmp in im_Parabasal of train set\n",
      "Processed 006_05.bmp in im_Parabasal of train set\n",
      "Processed 006_07.bmp in im_Parabasal of train set\n",
      "Processed 006_08.bmp in im_Parabasal of train set\n",
      "Processed 007_01.bmp in im_Parabasal of train set\n",
      "Processed 008_01.bmp in im_Parabasal of train set\n",
      "Processed 008_02.bmp in im_Parabasal of train set\n",
      "Processed 008_03.bmp in im_Parabasal of train set\n",
      "Processed 008_04.bmp in im_Parabasal of train set\n",
      "Processed 008_05.bmp in im_Parabasal of train set\n",
      "Processed 008_07.bmp in im_Parabasal of train set\n",
      "Processed 008_11.bmp in im_Parabasal of train set\n",
      "Processed 009_01.bmp in im_Parabasal of train set\n",
      "Processed 009_04.bmp in im_Parabasal of train set\n",
      "Processed 009_05.bmp in im_Parabasal of train set\n",
      "Processed 010_01.bmp in im_Parabasal of train set\n",
      "Processed 010_05.bmp in im_Parabasal of train set\n",
      "Processed 010_08.bmp in im_Parabasal of train set\n",
      "Processed 010_09.bmp in im_Parabasal of train set\n",
      "Processed 011_01.bmp in im_Parabasal of train set\n",
      "Processed 011_04.bmp in im_Parabasal of train set\n",
      "Processed 011_05.bmp in im_Parabasal of train set\n",
      "Processed 011_06.bmp in im_Parabasal of train set\n",
      "Processed 011_07.bmp in im_Parabasal of train set\n",
      "Processed 011_08.bmp in im_Parabasal of train set\n",
      "Processed 011_09.bmp in im_Parabasal of train set\n",
      "Processed 011_11.bmp in im_Parabasal of train set\n",
      "Processed 011_12.bmp in im_Parabasal of train set\n",
      "Processed 012_01.bmp in im_Parabasal of train set\n",
      "Processed 012_02.bmp in im_Parabasal of train set\n",
      "Processed 012_03.bmp in im_Parabasal of train set\n",
      "Processed 012_04.bmp in im_Parabasal of train set\n",
      "Processed 012_05.bmp in im_Parabasal of train set\n",
      "Processed 012_06.bmp in im_Parabasal of train set\n",
      "Processed 012_07.bmp in im_Parabasal of train set\n",
      "Processed 013_01.bmp in im_Parabasal of train set\n",
      "Processed 014_01.bmp in im_Parabasal of train set\n",
      "Processed 014_03.bmp in im_Parabasal of train set\n",
      "Processed 014_04.bmp in im_Parabasal of train set\n",
      "Processed 014_05.bmp in im_Parabasal of train set\n",
      "Processed 014_07.bmp in im_Parabasal of train set\n",
      "Processed 014_08.bmp in im_Parabasal of train set\n",
      "Processed 014_09.bmp in im_Parabasal of train set\n",
      "Processed 014_10.bmp in im_Parabasal of train set\n",
      "Processed 015_02.bmp in im_Parabasal of train set\n",
      "Processed 015_04.bmp in im_Parabasal of train set\n",
      "Processed 015_05.bmp in im_Parabasal of train set\n",
      "Processed 015_06.bmp in im_Parabasal of train set\n",
      "Processed 016_01.bmp in im_Parabasal of train set\n",
      "Processed 016_02.bmp in im_Parabasal of train set\n",
      "Processed 016_03.bmp in im_Parabasal of train set\n",
      "Processed 016_04.bmp in im_Parabasal of train set\n",
      "Processed 016_05.bmp in im_Parabasal of train set\n",
      "Processed 016_06.bmp in im_Parabasal of train set\n",
      "Processed 016_07.bmp in im_Parabasal of train set\n",
      "Processed 016_09.bmp in im_Parabasal of train set\n",
      "Processed 016_10.bmp in im_Parabasal of train set\n",
      "Processed 017_01.bmp in im_Parabasal of train set\n",
      "Processed 018_01.bmp in im_Parabasal of train set\n",
      "Processed 018_02.bmp in im_Parabasal of train set\n",
      "Processed 018_04.bmp in im_Parabasal of train set\n",
      "Processed 018_05.bmp in im_Parabasal of train set\n",
      "Processed 018_06.bmp in im_Parabasal of train set\n",
      "Processed 019_02.bmp in im_Parabasal of train set\n",
      "Processed 019_03.bmp in im_Parabasal of train set\n",
      "Processed 019_04.bmp in im_Parabasal of train set\n",
      "Processed 019_05.bmp in im_Parabasal of train set\n",
      "Processed 019_07.bmp in im_Parabasal of train set\n",
      "Processed 019_08.bmp in im_Parabasal of train set\n",
      "Processed 019_09.bmp in im_Parabasal of train set\n",
      "Processed 019_10.bmp in im_Parabasal of train set\n",
      "Processed 020_01.bmp in im_Parabasal of train set\n",
      "Processed 020_03.bmp in im_Parabasal of train set\n",
      "Processed 020_04.bmp in im_Parabasal of train set\n",
      "Processed 021_01.bmp in im_Parabasal of train set\n",
      "Processed 021_02.bmp in im_Parabasal of train set\n",
      "Processed 021_03.bmp in im_Parabasal of train set\n",
      "Processed 021_04.bmp in im_Parabasal of train set\n",
      "Processed 021_05.bmp in im_Parabasal of train set\n",
      "Processed 021_06.bmp in im_Parabasal of train set\n",
      "Processed 021_07.bmp in im_Parabasal of train set\n",
      "Processed 022_02.bmp in im_Parabasal of train set\n",
      "Processed 022_03.bmp in im_Parabasal of train set\n",
      "Processed 022_04.bmp in im_Parabasal of train set\n",
      "Processed 022_05.bmp in im_Parabasal of train set\n",
      "Processed 022_06.bmp in im_Parabasal of train set\n",
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      "Processed 104_02.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 104_03.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 104_04.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 105_01.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 105_02.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 105_03.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 105_04.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 106_01.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 106_02.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 106_03.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 106_04.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 106_06.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 106_08.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 106_11.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 106_12.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 107_01.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 107_04.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 107_05.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 107_06.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 108_01.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 108_02.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 108_03.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 108_04.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 108_05.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 108_06.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 108_07.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 108_08.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 109_01.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 109_02.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 110_01.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 110_02.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 111_02.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 111_03.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 111_05.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 111_06.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 112_01.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 112_02.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 112_03.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 112_04.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 112_05.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 112_06.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 112_08.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 113_02.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 113_04.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 113_06.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 114_01.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 114_02.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 114_03.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 114_05.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 114_06.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 114_07.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 114_08.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 115_01.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 115_03.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 115_04.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 115_05.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 115_06.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 116_01.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 116_02.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 116_03.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 116_04.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 116_05.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 117_01.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 117_02.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 117_03.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 118_01.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 118_03.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 118_05.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 118_06.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 118_08.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 118_09.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 119_01.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 119_03.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 119_04.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 119_06.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 119_10.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 120_01.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 120_02.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 120_05.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 121_03.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 121_04.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 121_05.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 121_07.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 121_08.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 122_02.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 122_03.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 122_05.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 122_06.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 122_07.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 122_08.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 123_01.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 123_02.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 123_03.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 123_04.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 123_06.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 123_08.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 124_02.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 124_03.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 124_04.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 125_01.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 125_03.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 125_04.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 126_01.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 126_05.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 126_06.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 126_07.bmp in im_Superficial-Intermediate of train set\n",
      "Processed 001_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 001_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 001_03.bmp in im_Dyskeratotic of val set\n",
      "Processed 001_05.bmp in im_Dyskeratotic of val set\n",
      "Processed 002_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 002_05.bmp in im_Dyskeratotic of val set\n",
      "Processed 003_11.bmp in im_Dyskeratotic of val set\n",
      "Processed 005_03.bmp in im_Dyskeratotic of val set\n",
      "Processed 006_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 006_04.bmp in im_Dyskeratotic of val set\n",
      "Processed 007_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 008_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 008_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 008_04.bmp in im_Dyskeratotic of val set\n",
      "Processed 009_03.bmp in im_Dyskeratotic of val set\n",
      "Processed 011_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 013_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 013_03.bmp in im_Dyskeratotic of val set\n",
      "Processed 013_05.bmp in im_Dyskeratotic of val set\n",
      "Processed 014_04.bmp in im_Dyskeratotic of val set\n",
      "Processed 015_04.bmp in im_Dyskeratotic of val set\n",
      "Processed 016_03.bmp in im_Dyskeratotic of val set\n",
      "Processed 017_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 018_03.bmp in im_Dyskeratotic of val set\n",
      "Processed 020_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 022_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 024_04.bmp in im_Dyskeratotic of val set\n",
      "Processed 025_05.bmp in im_Dyskeratotic of val set\n",
      "Processed 027_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 028_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 030_03.bmp in im_Dyskeratotic of val set\n",
      "Processed 032_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 037_07.bmp in im_Dyskeratotic of val set\n",
      "Processed 039_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 040_03.bmp in im_Dyskeratotic of val set\n",
      "Processed 044_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 047_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 048_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 048_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 058_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 059_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 059_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 059_05.bmp in im_Dyskeratotic of val set\n",
      "Processed 059_08.bmp in im_Dyskeratotic of val set\n",
      "Processed 062_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 062_03.bmp in im_Dyskeratotic of val set\n",
      "Processed 064_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 065_03.bmp in im_Dyskeratotic of val set\n",
      "Processed 066_04.bmp in im_Dyskeratotic of val set\n",
      "Processed 066_05.bmp in im_Dyskeratotic of val set\n",
      "Processed 066_13.bmp in im_Dyskeratotic of val set\n",
      "Processed 067_04.bmp in im_Dyskeratotic of val set\n",
      "Processed 067_10.bmp in im_Dyskeratotic of val set\n",
      "Processed 067_14.bmp in im_Dyskeratotic of val set\n",
      "Processed 067_16.bmp in im_Dyskeratotic of val set\n",
      "Processed 071_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 072_03.bmp in im_Dyskeratotic of val set\n",
      "Processed 072_07.bmp in im_Dyskeratotic of val set\n",
      "Processed 072_08.bmp in im_Dyskeratotic of val set\n",
      "Processed 072_09.bmp in im_Dyskeratotic of val set\n",
      "Processed 081_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 081_05.bmp in im_Dyskeratotic of val set\n",
      "Processed 085_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 086_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 088_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 088_03.bmp in im_Dyskeratotic of val set\n",
      "Processed 090_04.bmp in im_Dyskeratotic of val set\n",
      "Processed 092_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 097_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 099_03.bmp in im_Dyskeratotic of val set\n",
      "Processed 100_08.bmp in im_Dyskeratotic of val set\n",
      "Processed 101_08.bmp in im_Dyskeratotic of val set\n",
      "Processed 101_16.bmp in im_Dyskeratotic of val set\n",
      "Processed 102_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 102_08.bmp in im_Dyskeratotic of val set\n",
      "Processed 103_05.bmp in im_Dyskeratotic of val set\n",
      "Processed 104_06.bmp in im_Dyskeratotic of val set\n",
      "Processed 104_08.bmp in im_Dyskeratotic of val set\n",
      "Processed 105_04.bmp in im_Dyskeratotic of val set\n",
      "Processed 107_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 110_04.bmp in im_Dyskeratotic of val set\n",
      "Processed 111_04.bmp in im_Dyskeratotic of val set\n",
      "Processed 115_05.bmp in im_Dyskeratotic of val set\n",
      "Processed 116_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 117_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 117_04.bmp in im_Dyskeratotic of val set\n",
      "Processed 118_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 120_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 125_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 127_03.bmp in im_Dyskeratotic of val set\n",
      "Processed 135_04.bmp in im_Dyskeratotic of val set\n",
      "Processed 136_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 138_03.bmp in im_Dyskeratotic of val set\n",
      "Processed 138_06.bmp in im_Dyskeratotic of val set\n",
      "Processed 141_06.bmp in im_Dyskeratotic of val set\n",
      "Processed 141_08.bmp in im_Dyskeratotic of val set\n",
      "Processed 145_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 148_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 152_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 160_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 163_03.bmp in im_Dyskeratotic of val set\n",
      "Processed 163_04.bmp in im_Dyskeratotic of val set\n",
      "Processed 164_04.bmp in im_Dyskeratotic of val set\n",
      "Processed 164_13.bmp in im_Dyskeratotic of val set\n",
      "Processed 164_14.bmp in im_Dyskeratotic of val set\n",
      "Processed 164_21.bmp in im_Dyskeratotic of val set\n",
      "Processed 167_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 170_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 173_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 184_04.bmp in im_Dyskeratotic of val set\n",
      "Processed 208_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 210_03.bmp in im_Dyskeratotic of val set\n",
      "Processed 211_02.bmp in im_Dyskeratotic of val set\n",
      "Processed 211_05.bmp in im_Dyskeratotic of val set\n",
      "Processed 214_03.bmp in im_Dyskeratotic of val set\n",
      "Processed 216_03.bmp in im_Dyskeratotic of val set\n",
      "Processed 217_01.bmp in im_Dyskeratotic of val set\n",
      "Processed 219_05.bmp in im_Dyskeratotic of val set\n",
      "Processed 219_06.bmp in im_Dyskeratotic of val set\n",
      "Processed 220_04.bmp in im_Dyskeratotic of val set\n",
      "Processed 221_05.bmp in im_Dyskeratotic of val set\n",
      "Processed 221_06.bmp in im_Dyskeratotic of val set\n",
      "Processed 011_02.bmp in im_Koilocytotic of val set\n",
      "Processed 013_04.bmp in im_Koilocytotic of val set\n",
      "Processed 014_01.bmp in im_Koilocytotic of val set\n",
      "Processed 015_01.bmp in im_Koilocytotic of val set\n",
      "Processed 015_02.bmp in im_Koilocytotic of val set\n",
      "Processed 017_03.bmp in im_Koilocytotic of val set\n",
      "Processed 017_05.bmp in im_Koilocytotic of val set\n",
      "Processed 019_01.bmp in im_Koilocytotic of val set\n",
      "Processed 019_02.bmp in im_Koilocytotic of val set\n",
      "Processed 020_02.bmp in im_Koilocytotic of val set\n",
      "Processed 020_03.bmp in im_Koilocytotic of val set\n",
      "Processed 022_01.bmp in im_Koilocytotic of val set\n",
      "Processed 024_01.bmp in im_Koilocytotic of val set\n",
      "Processed 025_02.bmp in im_Koilocytotic of val set\n",
      "Processed 026_03.bmp in im_Koilocytotic of val set\n",
      "Processed 032_03.bmp in im_Koilocytotic of val set\n",
      "Processed 038_03.bmp in im_Koilocytotic of val set\n",
      "Processed 040_01.bmp in im_Koilocytotic of val set\n",
      "Processed 041_01.bmp in im_Koilocytotic of val set\n",
      "Processed 042_03.bmp in im_Koilocytotic of val set\n",
      "Processed 043_04.bmp in im_Koilocytotic of val set\n",
      "Processed 044_01.bmp in im_Koilocytotic of val set\n",
      "Processed 044_02.bmp in im_Koilocytotic of val set\n",
      "Processed 049_04.bmp in im_Koilocytotic of val set\n",
      "Processed 051_02.bmp in im_Koilocytotic of val set\n",
      "Processed 052_04.bmp in im_Koilocytotic of val set\n",
      "Processed 064_02.bmp in im_Koilocytotic of val set\n",
      "Processed 065_02.bmp in im_Koilocytotic of val set\n",
      "Processed 069_01.bmp in im_Koilocytotic of val set\n",
      "Processed 070_02.bmp in im_Koilocytotic of val set\n",
      "Processed 073_02.bmp in im_Koilocytotic of val set\n",
      "Processed 081_01.bmp in im_Koilocytotic of val set\n",
      "Processed 083_03.bmp in im_Koilocytotic of val set\n",
      "Processed 084_04.bmp in im_Koilocytotic of val set\n",
      "Processed 085_04.bmp in im_Koilocytotic of val set\n",
      "Processed 087_04.bmp in im_Koilocytotic of val set\n",
      "Processed 087_08.bmp in im_Koilocytotic of val set\n",
      "Processed 087_14.bmp in im_Koilocytotic of val set\n",
      "Processed 087_15.bmp in im_Koilocytotic of val set\n",
      "Processed 089_01.bmp in im_Koilocytotic of val set\n",
      "Processed 089_03.bmp in im_Koilocytotic of val set\n",
      "Processed 089_06.bmp in im_Koilocytotic of val set\n",
      "Processed 090_04.bmp in im_Koilocytotic of val set\n",
      "Processed 091_04.bmp in im_Koilocytotic of val set\n",
      "Processed 094_01.bmp in im_Koilocytotic of val set\n",
      "Processed 095_04.bmp in im_Koilocytotic of val set\n",
      "Processed 095_05.bmp in im_Koilocytotic of val set\n",
      "Processed 097_10.bmp in im_Koilocytotic of val set\n",
      "Processed 099_05.bmp in im_Koilocytotic of val set\n",
      "Processed 099_06.bmp in im_Koilocytotic of val set\n",
      "Processed 099_08.bmp in im_Koilocytotic of val set\n",
      "Processed 099_09.bmp in im_Koilocytotic of val set\n",
      "Processed 102_02.bmp in im_Koilocytotic of val set\n",
      "Processed 103_01.bmp in im_Koilocytotic of val set\n",
      "Processed 104_07.bmp in im_Koilocytotic of val set\n",
      "Processed 105_04.bmp in im_Koilocytotic of val set\n",
      "Processed 105_16.bmp in im_Koilocytotic of val set\n",
      "Processed 105_18.bmp in im_Koilocytotic of val set\n",
      "Processed 106_02.bmp in im_Koilocytotic of val set\n",
      "Processed 107_03.bmp in im_Koilocytotic of val set\n",
      "Processed 107_07.bmp in im_Koilocytotic of val set\n",
      "Processed 108_07.bmp in im_Koilocytotic of val set\n",
      "Processed 111_09.bmp in im_Koilocytotic of val set\n",
      "Processed 112_02.bmp in im_Koilocytotic of val set\n",
      "Processed 113_08.bmp in im_Koilocytotic of val set\n",
      "Processed 113_09.bmp in im_Koilocytotic of val set\n",
      "Processed 114_05.bmp in im_Koilocytotic of val set\n",
      "Processed 114_09.bmp in im_Koilocytotic of val set\n",
      "Processed 115_01.bmp in im_Koilocytotic of val set\n",
      "Processed 119_02.bmp in im_Koilocytotic of val set\n",
      "Processed 121_01.bmp in im_Koilocytotic of val set\n",
      "Processed 121_11.bmp in im_Koilocytotic of val set\n",
      "Processed 122_03.bmp in im_Koilocytotic of val set\n",
      "Processed 122_10.bmp in im_Koilocytotic of val set\n",
      "Processed 122_15.bmp in im_Koilocytotic of val set\n",
      "Processed 122_16.bmp in im_Koilocytotic of val set\n",
      "Processed 123_07.bmp in im_Koilocytotic of val set\n",
      "Processed 123_08.bmp in im_Koilocytotic of val set\n",
      "Processed 123_10.bmp in im_Koilocytotic of val set\n",
      "Processed 124_05.bmp in im_Koilocytotic of val set\n",
      "Processed 126_01.bmp in im_Koilocytotic of val set\n",
      "Processed 129_02.bmp in im_Koilocytotic of val set\n",
      "Processed 130_03.bmp in im_Koilocytotic of val set\n",
      "Processed 130_04.bmp in im_Koilocytotic of val set\n",
      "Processed 130_13.bmp in im_Koilocytotic of val set\n",
      "Processed 132_04.bmp in im_Koilocytotic of val set\n",
      "Processed 137_02.bmp in im_Koilocytotic of val set\n",
      "Processed 138_02.bmp in im_Koilocytotic of val set\n",
      "Processed 139_03.bmp in im_Koilocytotic of val set\n",
      "Processed 140_05.bmp in im_Koilocytotic of val set\n",
      "Processed 140_08.bmp in im_Koilocytotic of val set\n",
      "Processed 140_11.bmp in im_Koilocytotic of val set\n",
      "Processed 143_03.bmp in im_Koilocytotic of val set\n",
      "Processed 146_02.bmp in im_Koilocytotic of val set\n",
      "Processed 146_06.bmp in im_Koilocytotic of val set\n",
      "Processed 146_08.bmp in im_Koilocytotic of val set\n",
      "Processed 148_03.bmp in im_Koilocytotic of val set\n",
      "Processed 150_04.bmp in im_Koilocytotic of val set\n",
      "Processed 154_04.bmp in im_Koilocytotic of val set\n",
      "Processed 156_02.bmp in im_Koilocytotic of val set\n",
      "Processed 158_02.bmp in im_Koilocytotic of val set\n",
      "Processed 160_02.bmp in im_Koilocytotic of val set\n",
      "Processed 161_04.bmp in im_Koilocytotic of val set\n",
      "Processed 162_02.bmp in im_Koilocytotic of val set\n",
      "Processed 163_01.bmp in im_Koilocytotic of val set\n",
      "Processed 166_01.bmp in im_Koilocytotic of val set\n",
      "Processed 167_01.bmp in im_Koilocytotic of val set\n",
      "Processed 171_01.bmp in im_Koilocytotic of val set\n",
      "Processed 173_01.bmp in im_Koilocytotic of val set\n",
      "Processed 183_01.bmp in im_Koilocytotic of val set\n",
      "Processed 190_01.bmp in im_Koilocytotic of val set\n",
      "Processed 191_02.bmp in im_Koilocytotic of val set\n",
      "Processed 194_02.bmp in im_Koilocytotic of val set\n",
      "Processed 195_02.bmp in im_Koilocytotic of val set\n",
      "Processed 199_01.bmp in im_Koilocytotic of val set\n",
      "Processed 200_01.bmp in im_Koilocytotic of val set\n",
      "Processed 203_01.bmp in im_Koilocytotic of val set\n",
      "Processed 205_01.bmp in im_Koilocytotic of val set\n",
      "Processed 218_01.bmp in im_Koilocytotic of val set\n",
      "Processed 220_02.bmp in im_Koilocytotic of val set\n",
      "Processed 221_02.bmp in im_Koilocytotic of val set\n",
      "Processed 223_02.bmp in im_Koilocytotic of val set\n",
      "Processed 229_01.bmp in im_Koilocytotic of val set\n",
      "Processed 234_01.bmp in im_Koilocytotic of val set\n",
      "Processed 001_01.bmp in im_Metaplastic of val set\n",
      "Processed 001_13.bmp in im_Metaplastic of val set\n",
      "Processed 002_06.bmp in im_Metaplastic of val set\n",
      "Processed 002_09.bmp in im_Metaplastic of val set\n",
      "Processed 002_12.bmp in im_Metaplastic of val set\n",
      "Processed 006_04.bmp in im_Metaplastic of val set\n",
      "Processed 007_04.bmp in im_Metaplastic of val set\n",
      "Processed 008_02.bmp in im_Metaplastic of val set\n",
      "Processed 009_01.bmp in im_Metaplastic of val set\n",
      "Processed 011_01.bmp in im_Metaplastic of val set\n",
      "Processed 013_02.bmp in im_Metaplastic of val set\n",
      "Processed 015_03.bmp in im_Metaplastic of val set\n",
      "Processed 020_01.bmp in im_Metaplastic of val set\n",
      "Processed 024_02.bmp in im_Metaplastic of val set\n",
      "Processed 024_09.bmp in im_Metaplastic of val set\n",
      "Processed 025_08.bmp in im_Metaplastic of val set\n",
      "Processed 025_10.bmp in im_Metaplastic of val set\n",
      "Processed 027_01.bmp in im_Metaplastic of val set\n",
      "Processed 035_02.bmp in im_Metaplastic of val set\n",
      "Processed 040_01.bmp in im_Metaplastic of val set\n",
      "Processed 040_02.bmp in im_Metaplastic of val set\n",
      "Processed 042_01.bmp in im_Metaplastic of val set\n",
      "Processed 043_01.bmp in im_Metaplastic of val set\n",
      "Processed 044_01.bmp in im_Metaplastic of val set\n",
      "Processed 047_01.bmp in im_Metaplastic of val set\n",
      "Processed 047_02.bmp in im_Metaplastic of val set\n",
      "Processed 051_01.bmp in im_Metaplastic of val set\n",
      "Processed 056_01.bmp in im_Metaplastic of val set\n",
      "Processed 058_01.bmp in im_Metaplastic of val set\n",
      "Processed 059_01.bmp in im_Metaplastic of val set\n",
      "Processed 062_01.bmp in im_Metaplastic of val set\n",
      "Processed 064_01.bmp in im_Metaplastic of val set\n",
      "Processed 064_03.bmp in im_Metaplastic of val set\n",
      "Processed 066_02.bmp in im_Metaplastic of val set\n",
      "Processed 068_05.bmp in im_Metaplastic of val set\n",
      "Processed 069_01.bmp in im_Metaplastic of val set\n",
      "Processed 072_01.bmp in im_Metaplastic of val set\n",
      "Processed 072_05.bmp in im_Metaplastic of val set\n",
      "Processed 074_02.bmp in im_Metaplastic of val set\n",
      "Processed 074_05.bmp in im_Metaplastic of val set\n",
      "Processed 074_06.bmp in im_Metaplastic of val set\n",
      "Processed 080_01.bmp in im_Metaplastic of val set\n",
      "Processed 081_06.bmp in im_Metaplastic of val set\n",
      "Processed 082_02.bmp in im_Metaplastic of val set\n",
      "Processed 083_02.bmp in im_Metaplastic of val set\n",
      "Processed 083_05.bmp in im_Metaplastic of val set\n",
      "Processed 088_05.bmp in im_Metaplastic of val set\n",
      "Processed 088_06.bmp in im_Metaplastic of val set\n",
      "Processed 094_03.bmp in im_Metaplastic of val set\n",
      "Processed 096_02.bmp in im_Metaplastic of val set\n",
      "Processed 096_03.bmp in im_Metaplastic of val set\n",
      "Processed 096_04.bmp in im_Metaplastic of val set\n",
      "Processed 099_01.bmp in im_Metaplastic of val set\n",
      "Processed 101_02.bmp in im_Metaplastic of val set\n",
      "Processed 102_04.bmp in im_Metaplastic of val set\n",
      "Processed 109_01.bmp in im_Metaplastic of val set\n",
      "Processed 110_01.bmp in im_Metaplastic of val set\n",
      "Processed 114_02.bmp in im_Metaplastic of val set\n",
      "Processed 116_02.bmp in im_Metaplastic of val set\n",
      "Processed 117_02.bmp in im_Metaplastic of val set\n",
      "Processed 122_01.bmp in im_Metaplastic of val set\n",
      "Processed 125_01.bmp in im_Metaplastic of val set\n",
      "Processed 133_01.bmp in im_Metaplastic of val set\n",
      "Processed 134_02.bmp in im_Metaplastic of val set\n",
      "Processed 140_02.bmp in im_Metaplastic of val set\n",
      "Processed 141_05.bmp in im_Metaplastic of val set\n",
      "Processed 143_03.bmp in im_Metaplastic of val set\n",
      "Processed 148_04.bmp in im_Metaplastic of val set\n",
      "Processed 150_01.bmp in im_Metaplastic of val set\n",
      "Processed 150_02.bmp in im_Metaplastic of val set\n",
      "Processed 152_03.bmp in im_Metaplastic of val set\n",
      "Processed 162_01.bmp in im_Metaplastic of val set\n",
      "Processed 164_02.bmp in im_Metaplastic of val set\n",
      "Processed 164_03.bmp in im_Metaplastic of val set\n",
      "Processed 164_05.bmp in im_Metaplastic of val set\n",
      "Processed 165_01.bmp in im_Metaplastic of val set\n",
      "Processed 167_02.bmp in im_Metaplastic of val set\n",
      "Processed 168_01.bmp in im_Metaplastic of val set\n",
      "Processed 171_02.bmp in im_Metaplastic of val set\n",
      "Processed 171_03.bmp in im_Metaplastic of val set\n",
      "Processed 176_03.bmp in im_Metaplastic of val set\n",
      "Processed 182_01.bmp in im_Metaplastic of val set\n",
      "Processed 185_01.bmp in im_Metaplastic of val set\n",
      "Processed 185_05.bmp in im_Metaplastic of val set\n",
      "Processed 188_01.bmp in im_Metaplastic of val set\n",
      "Processed 189_01.bmp in im_Metaplastic of val set\n",
      "Processed 190_01.bmp in im_Metaplastic of val set\n",
      "Processed 193_06.bmp in im_Metaplastic of val set\n",
      "Processed 196_02.bmp in im_Metaplastic of val set\n",
      "Processed 201_01.bmp in im_Metaplastic of val set\n",
      "Processed 203_03.bmp in im_Metaplastic of val set\n",
      "Processed 204_02.bmp in im_Metaplastic of val set\n",
      "Processed 205_02.bmp in im_Metaplastic of val set\n",
      "Processed 207_01.bmp in im_Metaplastic of val set\n",
      "Processed 209_02.bmp in im_Metaplastic of val set\n",
      "Processed 216_02.bmp in im_Metaplastic of val set\n",
      "Processed 218_03.bmp in im_Metaplastic of val set\n",
      "Processed 219_01.bmp in im_Metaplastic of val set\n",
      "Processed 222_02.bmp in im_Metaplastic of val set\n",
      "Processed 225_04.bmp in im_Metaplastic of val set\n",
      "Processed 227_02.bmp in im_Metaplastic of val set\n",
      "Processed 230_01.bmp in im_Metaplastic of val set\n",
      "Processed 235_02.bmp in im_Metaplastic of val set\n",
      "Processed 238_01.bmp in im_Metaplastic of val set\n",
      "Processed 239_03.bmp in im_Metaplastic of val set\n",
      "Processed 240_02.bmp in im_Metaplastic of val set\n",
      "Processed 241_01.bmp in im_Metaplastic of val set\n",
      "Processed 246_03.bmp in im_Metaplastic of val set\n",
      "Processed 247_02.bmp in im_Metaplastic of val set\n",
      "Processed 250_03.bmp in im_Metaplastic of val set\n",
      "Processed 254_03.bmp in im_Metaplastic of val set\n",
      "Processed 256_01.bmp in im_Metaplastic of val set\n",
      "Processed 256_03.bmp in im_Metaplastic of val set\n",
      "Processed 259_02.bmp in im_Metaplastic of val set\n",
      "Processed 261_02.bmp in im_Metaplastic of val set\n",
      "Processed 265_03.bmp in im_Metaplastic of val set\n",
      "Processed 266_01.bmp in im_Metaplastic of val set\n",
      "Processed 268_04.bmp in im_Metaplastic of val set\n",
      "Processed 269_02.bmp in im_Metaplastic of val set\n",
      "Processed 001_02.bmp in im_Parabasal of val set\n",
      "Processed 003_04.bmp in im_Parabasal of val set\n",
      "Processed 003_05.bmp in im_Parabasal of val set\n",
      "Processed 003_08.bmp in im_Parabasal of val set\n",
      "Processed 003_09.bmp in im_Parabasal of val set\n",
      "Processed 003_12.bmp in im_Parabasal of val set\n",
      "Processed 004_03.bmp in im_Parabasal of val set\n",
      "Processed 004_12.bmp in im_Parabasal of val set\n",
      "Processed 004_13.bmp in im_Parabasal of val set\n",
      "Processed 008_06.bmp in im_Parabasal of val set\n",
      "Processed 009_02.bmp in im_Parabasal of val set\n",
      "Processed 009_03.bmp in im_Parabasal of val set\n",
      "Processed 009_06.bmp in im_Parabasal of val set\n",
      "Processed 010_06.bmp in im_Parabasal of val set\n",
      "Processed 011_03.bmp in im_Parabasal of val set\n",
      "Processed 011_10.bmp in im_Parabasal of val set\n",
      "Processed 013_02.bmp in im_Parabasal of val set\n",
      "Processed 013_03.bmp in im_Parabasal of val set\n",
      "Processed 014_06.bmp in im_Parabasal of val set\n",
      "Processed 016_08.bmp in im_Parabasal of val set\n",
      "Processed 016_11.bmp in im_Parabasal of val set\n",
      "Processed 016_12.bmp in im_Parabasal of val set\n",
      "Processed 018_03.bmp in im_Parabasal of val set\n",
      "Processed 019_01.bmp in im_Parabasal of val set\n",
      "Processed 019_06.bmp in im_Parabasal of val set\n",
      "Processed 019_11.bmp in im_Parabasal of val set\n",
      "Processed 020_02.bmp in im_Parabasal of val set\n",
      "Processed 020_05.bmp in im_Parabasal of val set\n",
      "Processed 021_08.bmp in im_Parabasal of val set\n",
      "Processed 023_03.bmp in im_Parabasal of val set\n",
      "Processed 023_04.bmp in im_Parabasal of val set\n",
      "Processed 026_06.bmp in im_Parabasal of val set\n",
      "Processed 026_07.bmp in im_Parabasal of val set\n",
      "Processed 027_04.bmp in im_Parabasal of val set\n",
      "Processed 028_03.bmp in im_Parabasal of val set\n",
      "Processed 028_04.bmp in im_Parabasal of val set\n",
      "Processed 028_07.bmp in im_Parabasal of val set\n",
      "Processed 029_05.bmp in im_Parabasal of val set\n",
      "Processed 032_09.bmp in im_Parabasal of val set\n",
      "Processed 034_02.bmp in im_Parabasal of val set\n",
      "Processed 034_06.bmp in im_Parabasal of val set\n",
      "Processed 035_01.bmp in im_Parabasal of val set\n",
      "Processed 036_03.bmp in im_Parabasal of val set\n",
      "Processed 037_05.bmp in im_Parabasal of val set\n",
      "Processed 038_01.bmp in im_Parabasal of val set\n",
      "Processed 041_03.bmp in im_Parabasal of val set\n",
      "Processed 042_02.bmp in im_Parabasal of val set\n",
      "Processed 042_11.bmp in im_Parabasal of val set\n",
      "Processed 044_02.bmp in im_Parabasal of val set\n",
      "Processed 044_08.bmp in im_Parabasal of val set\n",
      "Processed 044_09.bmp in im_Parabasal of val set\n",
      "Processed 045_02.bmp in im_Parabasal of val set\n",
      "Processed 046_03.bmp in im_Parabasal of val set\n",
      "Processed 048_02.bmp in im_Parabasal of val set\n",
      "Processed 050_01.bmp in im_Parabasal of val set\n",
      "Processed 050_02.bmp in im_Parabasal of val set\n",
      "Processed 051_02.bmp in im_Parabasal of val set\n",
      "Processed 051_04.bmp in im_Parabasal of val set\n",
      "Processed 053_04.bmp in im_Parabasal of val set\n",
      "Processed 053_06.bmp in im_Parabasal of val set\n",
      "Processed 054_02.bmp in im_Parabasal of val set\n",
      "Processed 055_05.bmp in im_Parabasal of val set\n",
      "Processed 056_06.bmp in im_Parabasal of val set\n",
      "Processed 060_05.bmp in im_Parabasal of val set\n",
      "Processed 060_06.bmp in im_Parabasal of val set\n",
      "Processed 061_04.bmp in im_Parabasal of val set\n",
      "Processed 062_03.bmp in im_Parabasal of val set\n",
      "Processed 064_05.bmp in im_Parabasal of val set\n",
      "Processed 064_06.bmp in im_Parabasal of val set\n",
      "Processed 066_03.bmp in im_Parabasal of val set\n",
      "Processed 066_05.bmp in im_Parabasal of val set\n",
      "Processed 069_01.bmp in im_Parabasal of val set\n",
      "Processed 069_09.bmp in im_Parabasal of val set\n",
      "Processed 069_10.bmp in im_Parabasal of val set\n",
      "Processed 069_12.bmp in im_Parabasal of val set\n",
      "Processed 070_04.bmp in im_Parabasal of val set\n",
      "Processed 070_05.bmp in im_Parabasal of val set\n",
      "Processed 071_01.bmp in im_Parabasal of val set\n",
      "Processed 071_06.bmp in im_Parabasal of val set\n",
      "Processed 075_03.bmp in im_Parabasal of val set\n",
      "Processed 075_04.bmp in im_Parabasal of val set\n",
      "Processed 077_01.bmp in im_Parabasal of val set\n",
      "Processed 077_03.bmp in im_Parabasal of val set\n",
      "Processed 077_05.bmp in im_Parabasal of val set\n",
      "Processed 078_02.bmp in im_Parabasal of val set\n",
      "Processed 078_06.bmp in im_Parabasal of val set\n",
      "Processed 082_03.bmp in im_Parabasal of val set\n",
      "Processed 086_05.bmp in im_Parabasal of val set\n",
      "Processed 086_11.bmp in im_Parabasal of val set\n",
      "Processed 087_01.bmp in im_Parabasal of val set\n",
      "Processed 089_05.bmp in im_Parabasal of val set\n",
      "Processed 089_10.bmp in im_Parabasal of val set\n",
      "Processed 089_11.bmp in im_Parabasal of val set\n",
      "Processed 091_01.bmp in im_Parabasal of val set\n",
      "Processed 093_08.bmp in im_Parabasal of val set\n",
      "Processed 093_20.bmp in im_Parabasal of val set\n",
      "Processed 093_21.bmp in im_Parabasal of val set\n",
      "Processed 095_16.bmp in im_Parabasal of val set\n",
      "Processed 096_01.bmp in im_Parabasal of val set\n",
      "Processed 096_04.bmp in im_Parabasal of val set\n",
      "Processed 096_08.bmp in im_Parabasal of val set\n",
      "Processed 097_05.bmp in im_Parabasal of val set\n",
      "Processed 097_09.bmp in im_Parabasal of val set\n",
      "Processed 097_16.bmp in im_Parabasal of val set\n",
      "Processed 098_03.bmp in im_Parabasal of val set\n",
      "Processed 098_07.bmp in im_Parabasal of val set\n",
      "Processed 099_04.bmp in im_Parabasal of val set\n",
      "Processed 099_10.bmp in im_Parabasal of val set\n",
      "Processed 099_13.bmp in im_Parabasal of val set\n",
      "Processed 100_04.bmp in im_Parabasal of val set\n",
      "Processed 100_16.bmp in im_Parabasal of val set\n",
      "Processed 100_17.bmp in im_Parabasal of val set\n",
      "Processed 102_04.bmp in im_Parabasal of val set\n",
      "Processed 102_15.bmp in im_Parabasal of val set\n",
      "Processed 104_01.bmp in im_Parabasal of val set\n",
      "Processed 104_02.bmp in im_Parabasal of val set\n",
      "Processed 104_03.bmp in im_Parabasal of val set\n",
      "Processed 106_05.bmp in im_Parabasal of val set\n",
      "Processed 003_01.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 004_01.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 004_08.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 007_04.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 008_07.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 009_05.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 009_06.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 009_08.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 009_10.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 010_01.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 010_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 010_07.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 010_09.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 011_03.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 014_09.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 015_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 017_10.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 017_12.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 017_20.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 018_05.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 019_06.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 020_07.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 022_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 023_01.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 023_06.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 023_11.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 026_04.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 028_08.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 029_12.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 030_04.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 031_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 031_03.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 032_03.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 032_08.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 035_01.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 036_04.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 038_01.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 045_11.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 046_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 046_03.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 047_03.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 047_04.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 048_01.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 048_04.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 050_01.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 050_03.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 053_01.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 053_04.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 053_05.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 054_01.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 054_03.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 055_05.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 056_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 056_03.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 057_09.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 058_03.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 058_04.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 059_01.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 059_06.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 059_07.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 060_06.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 060_07.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 061_01.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 061_10.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 062_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 063_05.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 066_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 066_11.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 067_03.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 067_04.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 067_05.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 068_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 069_06.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 069_09.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 070_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 071_04.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 071_06.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 072_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 074_03.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 075_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 075_05.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 078_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 079_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 080_06.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 082_01.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 082_06.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 084_03.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 087_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 090_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 093_03.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 094_08.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 095_04.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 096_08.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 097_04.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 098_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 099_05.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 099_09.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 100_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 100_04.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 101_04.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 101_06.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 101_07.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 102_06.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 106_05.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 106_09.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 106_10.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 106_13.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 107_03.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 107_07.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 109_04.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 111_01.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 113_01.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 113_05.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 115_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 117_04.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 118_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 119_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 119_08.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 121_01.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 121_06.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 122_09.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 123_07.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 124_05.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 124_06.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 125_02.bmp in im_Superficial-Intermediate of val set\n",
      "Processed 002_06.bmp in im_Dyskeratotic of test set\n",
      "Processed 003_09.bmp in im_Dyskeratotic of test set\n",
      "Processed 005_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 005_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 005_04.bmp in im_Dyskeratotic of test set\n",
      "Processed 006_03.bmp in im_Dyskeratotic of test set\n",
      "Processed 008_03.bmp in im_Dyskeratotic of test set\n",
      "Processed 009_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 010_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 010_03.bmp in im_Dyskeratotic of test set\n",
      "Processed 010_04.bmp in im_Dyskeratotic of test set\n",
      "Processed 010_05.bmp in im_Dyskeratotic of test set\n",
      "Processed 011_03.bmp in im_Dyskeratotic of test set\n",
      "Processed 011_07.bmp in im_Dyskeratotic of test set\n",
      "Processed 011_08.bmp in im_Dyskeratotic of test set\n",
      "Processed 011_09.bmp in im_Dyskeratotic of test set\n",
      "Processed 012_04.bmp in im_Dyskeratotic of test set\n",
      "Processed 013_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 015_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 015_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 019_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 019_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 021_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 024_08.bmp in im_Dyskeratotic of test set\n",
      "Processed 025_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 029_03.bmp in im_Dyskeratotic of test set\n",
      "Processed 031_08.bmp in im_Dyskeratotic of test set\n",
      "Processed 034_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 038_03.bmp in im_Dyskeratotic of test set\n",
      "Processed 039_03.bmp in im_Dyskeratotic of test set\n",
      "Processed 041_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 041_06.bmp in im_Dyskeratotic of test set\n",
      "Processed 041_07.bmp in im_Dyskeratotic of test set\n",
      "Processed 042_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 042_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 043_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 043_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 052_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 054_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 059_11.bmp in im_Dyskeratotic of test set\n",
      "Processed 062_04.bmp in im_Dyskeratotic of test set\n",
      "Processed 062_05.bmp in im_Dyskeratotic of test set\n",
      "Processed 066_03.bmp in im_Dyskeratotic of test set\n",
      "Processed 066_14.bmp in im_Dyskeratotic of test set\n",
      "Processed 067_05.bmp in im_Dyskeratotic of test set\n",
      "Processed 067_08.bmp in im_Dyskeratotic of test set\n",
      "Processed 068_04.bmp in im_Dyskeratotic of test set\n",
      "Processed 072_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 072_04.bmp in im_Dyskeratotic of test set\n",
      "Processed 073_05.bmp in im_Dyskeratotic of test set\n",
      "Processed 075_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 076_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 079_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 081_04.bmp in im_Dyskeratotic of test set\n",
      "Processed 092_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 097_03.bmp in im_Dyskeratotic of test set\n",
      "Processed 097_05.bmp in im_Dyskeratotic of test set\n",
      "Processed 100_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 100_07.bmp in im_Dyskeratotic of test set\n",
      "Processed 101_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 101_13.bmp in im_Dyskeratotic of test set\n",
      "Processed 101_14.bmp in im_Dyskeratotic of test set\n",
      "Processed 102_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 102_07.bmp in im_Dyskeratotic of test set\n",
      "Processed 102_10.bmp in im_Dyskeratotic of test set\n",
      "Processed 104_04.bmp in im_Dyskeratotic of test set\n",
      "Processed 104_10.bmp in im_Dyskeratotic of test set\n",
      "Processed 105_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 107_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 112_07.bmp in im_Dyskeratotic of test set\n",
      "Processed 113_04.bmp in im_Dyskeratotic of test set\n",
      "Processed 113_05.bmp in im_Dyskeratotic of test set\n",
      "Processed 114_08.bmp in im_Dyskeratotic of test set\n",
      "Processed 114_09.bmp in im_Dyskeratotic of test set\n",
      "Processed 114_11.bmp in im_Dyskeratotic of test set\n",
      "Processed 119_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 124_04.bmp in im_Dyskeratotic of test set\n",
      "Processed 125_04.bmp in im_Dyskeratotic of test set\n",
      "Processed 126_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 129_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 131_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 135_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 140_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 141_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 141_11.bmp in im_Dyskeratotic of test set\n",
      "Processed 142_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 142_04.bmp in im_Dyskeratotic of test set\n",
      "Processed 145_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 146_04.bmp in im_Dyskeratotic of test set\n",
      "Processed 147_03.bmp in im_Dyskeratotic of test set\n",
      "Processed 150_03.bmp in im_Dyskeratotic of test set\n",
      "Processed 151_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 156_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 158_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 164_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 164_03.bmp in im_Dyskeratotic of test set\n",
      "Processed 164_10.bmp in im_Dyskeratotic of test set\n",
      "Processed 164_18.bmp in im_Dyskeratotic of test set\n",
      "Processed 164_23.bmp in im_Dyskeratotic of test set\n",
      "Processed 166_07.bmp in im_Dyskeratotic of test set\n",
      "Processed 170_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 172_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 173_04.bmp in im_Dyskeratotic of test set\n",
      "Processed 178_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 190_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 194_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 199_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 199_04.bmp in im_Dyskeratotic of test set\n",
      "Processed 200_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 202_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 209_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 211_06.bmp in im_Dyskeratotic of test set\n",
      "Processed 213_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 215_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 215_03.bmp in im_Dyskeratotic of test set\n",
      "Processed 217_03.bmp in im_Dyskeratotic of test set\n",
      "Processed 217_04.bmp in im_Dyskeratotic of test set\n",
      "Processed 220_02.bmp in im_Dyskeratotic of test set\n",
      "Processed 220_05.bmp in im_Dyskeratotic of test set\n",
      "Processed 221_03.bmp in im_Dyskeratotic of test set\n",
      "Processed 221_08.bmp in im_Dyskeratotic of test set\n",
      "Processed 222_01.bmp in im_Dyskeratotic of test set\n",
      "Processed 005_02.bmp in im_Koilocytotic of test set\n",
      "Processed 007_03.bmp in im_Koilocytotic of test set\n",
      "Processed 013_02.bmp in im_Koilocytotic of test set\n",
      "Processed 015_04.bmp in im_Koilocytotic of test set\n",
      "Processed 015_05.bmp in im_Koilocytotic of test set\n",
      "Processed 015_07.bmp in im_Koilocytotic of test set\n",
      "Processed 017_01.bmp in im_Koilocytotic of test set\n",
      "Processed 020_01.bmp in im_Koilocytotic of test set\n",
      "Processed 021_02.bmp in im_Koilocytotic of test set\n",
      "Processed 025_01.bmp in im_Koilocytotic of test set\n",
      "Processed 025_03.bmp in im_Koilocytotic of test set\n",
      "Processed 025_04.bmp in im_Koilocytotic of test set\n",
      "Processed 025_05.bmp in im_Koilocytotic of test set\n",
      "Processed 026_04.bmp in im_Koilocytotic of test set\n",
      "Processed 028_01.bmp in im_Koilocytotic of test set\n",
      "Processed 028_02.bmp in im_Koilocytotic of test set\n",
      "Processed 029_01.bmp in im_Koilocytotic of test set\n",
      "Processed 030_04.bmp in im_Koilocytotic of test set\n",
      "Processed 030_06.bmp in im_Koilocytotic of test set\n",
      "Processed 034_03.bmp in im_Koilocytotic of test set\n",
      "Processed 035_01.bmp in im_Koilocytotic of test set\n",
      "Processed 042_01.bmp in im_Koilocytotic of test set\n",
      "Processed 042_02.bmp in im_Koilocytotic of test set\n",
      "Processed 045_02.bmp in im_Koilocytotic of test set\n",
      "Processed 051_04.bmp in im_Koilocytotic of test set\n",
      "Processed 052_02.bmp in im_Koilocytotic of test set\n",
      "Processed 060_01.bmp in im_Koilocytotic of test set\n",
      "Processed 068_01.bmp in im_Koilocytotic of test set\n",
      "Processed 071_02.bmp in im_Koilocytotic of test set\n",
      "Processed 077_05.bmp in im_Koilocytotic of test set\n",
      "Processed 078_02.bmp in im_Koilocytotic of test set\n",
      "Processed 080_02.bmp in im_Koilocytotic of test set\n",
      "Processed 082_03.bmp in im_Koilocytotic of test set\n",
      "Processed 083_01.bmp in im_Koilocytotic of test set\n",
      "Processed 083_02.bmp in im_Koilocytotic of test set\n",
      "Processed 083_05.bmp in im_Koilocytotic of test set\n",
      "Processed 084_01.bmp in im_Koilocytotic of test set\n",
      "Processed 084_03.bmp in im_Koilocytotic of test set\n",
      "Processed 084_06.bmp in im_Koilocytotic of test set\n",
      "Processed 087_03.bmp in im_Koilocytotic of test set\n",
      "Processed 088_07.bmp in im_Koilocytotic of test set\n",
      "Processed 088_11.bmp in im_Koilocytotic of test set\n",
      "Processed 090_05.bmp in im_Koilocytotic of test set\n",
      "Processed 090_06.bmp in im_Koilocytotic of test set\n",
      "Processed 092_03.bmp in im_Koilocytotic of test set\n",
      "Processed 093_06.bmp in im_Koilocytotic of test set\n",
      "Processed 093_10.bmp in im_Koilocytotic of test set\n",
      "Processed 094_07.bmp in im_Koilocytotic of test set\n",
      "Processed 095_13.bmp in im_Koilocytotic of test set\n",
      "Processed 097_01.bmp in im_Koilocytotic of test set\n",
      "Processed 097_09.bmp in im_Koilocytotic of test set\n",
      "Processed 098_04.bmp in im_Koilocytotic of test set\n",
      "Processed 098_05.bmp in im_Koilocytotic of test set\n",
      "Processed 099_02.bmp in im_Koilocytotic of test set\n",
      "Processed 099_04.bmp in im_Koilocytotic of test set\n",
      "Processed 100_03.bmp in im_Koilocytotic of test set\n",
      "Processed 101_03.bmp in im_Koilocytotic of test set\n",
      "Processed 102_06.bmp in im_Koilocytotic of test set\n",
      "Processed 102_07.bmp in im_Koilocytotic of test set\n",
      "Processed 105_08.bmp in im_Koilocytotic of test set\n",
      "Processed 105_09.bmp in im_Koilocytotic of test set\n",
      "Processed 105_10.bmp in im_Koilocytotic of test set\n",
      "Processed 105_22.bmp in im_Koilocytotic of test set\n",
      "Processed 108_04.bmp in im_Koilocytotic of test set\n",
      "Processed 108_12.bmp in im_Koilocytotic of test set\n",
      "Processed 111_03.bmp in im_Koilocytotic of test set\n",
      "Processed 111_07.bmp in im_Koilocytotic of test set\n",
      "Processed 112_01.bmp in im_Koilocytotic of test set\n",
      "Processed 113_02.bmp in im_Koilocytotic of test set\n",
      "Processed 113_05.bmp in im_Koilocytotic of test set\n",
      "Processed 113_06.bmp in im_Koilocytotic of test set\n",
      "Processed 114_01.bmp in im_Koilocytotic of test set\n",
      "Processed 114_13.bmp in im_Koilocytotic of test set\n",
      "Processed 118_03.bmp in im_Koilocytotic of test set\n",
      "Processed 121_02.bmp in im_Koilocytotic of test set\n",
      "Processed 121_03.bmp in im_Koilocytotic of test set\n",
      "Processed 121_05.bmp in im_Koilocytotic of test set\n",
      "Processed 121_09.bmp in im_Koilocytotic of test set\n",
      "Processed 121_14.bmp in im_Koilocytotic of test set\n",
      "Processed 122_07.bmp in im_Koilocytotic of test set\n",
      "Processed 123_03.bmp in im_Koilocytotic of test set\n",
      "Processed 123_12.bmp in im_Koilocytotic of test set\n",
      "Processed 123_14.bmp in im_Koilocytotic of test set\n",
      "Processed 123_15.bmp in im_Koilocytotic of test set\n",
      "Processed 124_04.bmp in im_Koilocytotic of test set\n",
      "Processed 126_04.bmp in im_Koilocytotic of test set\n",
      "Processed 127_01.bmp in im_Koilocytotic of test set\n",
      "Processed 128_02.bmp in im_Koilocytotic of test set\n",
      "Processed 128_08.bmp in im_Koilocytotic of test set\n",
      "Processed 131_04.bmp in im_Koilocytotic of test set\n",
      "Processed 139_05.bmp in im_Koilocytotic of test set\n",
      "Processed 139_06.bmp in im_Koilocytotic of test set\n",
      "Processed 140_01.bmp in im_Koilocytotic of test set\n",
      "Processed 140_06.bmp in im_Koilocytotic of test set\n",
      "Processed 141_02.bmp in im_Koilocytotic of test set\n",
      "Processed 144_01.bmp in im_Koilocytotic of test set\n",
      "Processed 144_03.bmp in im_Koilocytotic of test set\n",
      "Processed 147_05.bmp in im_Koilocytotic of test set\n",
      "Processed 149_02.bmp in im_Koilocytotic of test set\n",
      "Processed 151_01.bmp in im_Koilocytotic of test set\n",
      "Processed 151_03.bmp in im_Koilocytotic of test set\n",
      "Processed 151_05.bmp in im_Koilocytotic of test set\n",
      "Processed 153_01.bmp in im_Koilocytotic of test set\n",
      "Processed 155_02.bmp in im_Koilocytotic of test set\n",
      "Processed 157_05.bmp in im_Koilocytotic of test set\n",
      "Processed 161_03.bmp in im_Koilocytotic of test set\n",
      "Processed 161_05.bmp in im_Koilocytotic of test set\n",
      "Processed 163_03.bmp in im_Koilocytotic of test set\n",
      "Processed 164_02.bmp in im_Koilocytotic of test set\n",
      "Processed 168_01.bmp in im_Koilocytotic of test set\n",
      "Processed 176_01.bmp in im_Koilocytotic of test set\n",
      "Processed 179_01.bmp in im_Koilocytotic of test set\n",
      "Processed 180_02.bmp in im_Koilocytotic of test set\n",
      "Processed 204_01.bmp in im_Koilocytotic of test set\n",
      "Processed 208_01.bmp in im_Koilocytotic of test set\n",
      "Processed 214_01.bmp in im_Koilocytotic of test set\n",
      "Processed 215_01.bmp in im_Koilocytotic of test set\n",
      "Processed 217_01.bmp in im_Koilocytotic of test set\n",
      "Processed 217_03.bmp in im_Koilocytotic of test set\n",
      "Processed 222_02.bmp in im_Koilocytotic of test set\n",
      "Processed 230_01.bmp in im_Koilocytotic of test set\n",
      "Processed 232_03.bmp in im_Koilocytotic of test set\n",
      "Processed 235_01.bmp in im_Koilocytotic of test set\n",
      "Processed 236_01.bmp in im_Koilocytotic of test set\n",
      "Processed 001_11.bmp in im_Metaplastic of test set\n",
      "Processed 002_11.bmp in im_Metaplastic of test set\n",
      "Processed 005_01.bmp in im_Metaplastic of test set\n",
      "Processed 006_01.bmp in im_Metaplastic of test set\n",
      "Processed 006_03.bmp in im_Metaplastic of test set\n",
      "Processed 008_01.bmp in im_Metaplastic of test set\n",
      "Processed 013_01.bmp in im_Metaplastic of test set\n",
      "Processed 014_02.bmp in im_Metaplastic of test set\n",
      "Processed 017_01.bmp in im_Metaplastic of test set\n",
      "Processed 018_02.bmp in im_Metaplastic of test set\n",
      "Processed 018_03.bmp in im_Metaplastic of test set\n",
      "Processed 021_01.bmp in im_Metaplastic of test set\n",
      "Processed 023_03.bmp in im_Metaplastic of test set\n",
      "Processed 023_04.bmp in im_Metaplastic of test set\n",
      "Processed 023_05.bmp in im_Metaplastic of test set\n",
      "Processed 024_03.bmp in im_Metaplastic of test set\n",
      "Processed 024_05.bmp in im_Metaplastic of test set\n",
      "Processed 025_06.bmp in im_Metaplastic of test set\n",
      "Processed 025_07.bmp in im_Metaplastic of test set\n",
      "Processed 027_04.bmp in im_Metaplastic of test set\n",
      "Processed 027_05.bmp in im_Metaplastic of test set\n",
      "Processed 035_01.bmp in im_Metaplastic of test set\n",
      "Processed 046_01.bmp in im_Metaplastic of test set\n",
      "Processed 057_02.bmp in im_Metaplastic of test set\n",
      "Processed 066_01.bmp in im_Metaplastic of test set\n",
      "Processed 067_05.bmp in im_Metaplastic of test set\n",
      "Processed 072_03.bmp in im_Metaplastic of test set\n",
      "Processed 072_04.bmp in im_Metaplastic of test set\n",
      "Processed 072_09.bmp in im_Metaplastic of test set\n",
      "Processed 072_13.bmp in im_Metaplastic of test set\n",
      "Processed 072_15.bmp in im_Metaplastic of test set\n",
      "Processed 073_01.bmp in im_Metaplastic of test set\n",
      "Processed 073_05.bmp in im_Metaplastic of test set\n",
      "Processed 077_04.bmp in im_Metaplastic of test set\n",
      "Processed 077_08.bmp in im_Metaplastic of test set\n",
      "Processed 079_03.bmp in im_Metaplastic of test set\n",
      "Processed 080_02.bmp in im_Metaplastic of test set\n",
      "Processed 080_06.bmp in im_Metaplastic of test set\n",
      "Processed 081_05.bmp in im_Metaplastic of test set\n",
      "Processed 083_03.bmp in im_Metaplastic of test set\n",
      "Processed 088_04.bmp in im_Metaplastic of test set\n",
      "Processed 089_02.bmp in im_Metaplastic of test set\n",
      "Processed 091_03.bmp in im_Metaplastic of test set\n",
      "Processed 093_02.bmp in im_Metaplastic of test set\n",
      "Processed 097_06.bmp in im_Metaplastic of test set\n",
      "Processed 100_02.bmp in im_Metaplastic of test set\n",
      "Processed 100_03.bmp in im_Metaplastic of test set\n",
      "Processed 100_04.bmp in im_Metaplastic of test set\n",
      "Processed 101_05.bmp in im_Metaplastic of test set\n",
      "Processed 102_02.bmp in im_Metaplastic of test set\n",
      "Processed 104_01.bmp in im_Metaplastic of test set\n",
      "Processed 113_01.bmp in im_Metaplastic of test set\n",
      "Processed 114_01.bmp in im_Metaplastic of test set\n",
      "Processed 123_01.bmp in im_Metaplastic of test set\n",
      "Processed 126_01.bmp in im_Metaplastic of test set\n",
      "Processed 141_07.bmp in im_Metaplastic of test set\n",
      "Processed 142_01.bmp in im_Metaplastic of test set\n",
      "Processed 143_02.bmp in im_Metaplastic of test set\n",
      "Processed 146_03.bmp in im_Metaplastic of test set\n",
      "Processed 147_01.bmp in im_Metaplastic of test set\n",
      "Processed 148_07.bmp in im_Metaplastic of test set\n",
      "Processed 149_01.bmp in im_Metaplastic of test set\n",
      "Processed 149_02.bmp in im_Metaplastic of test set\n",
      "Processed 153_02.bmp in im_Metaplastic of test set\n",
      "Processed 157_01.bmp in im_Metaplastic of test set\n",
      "Processed 160_02.bmp in im_Metaplastic of test set\n",
      "Processed 164_01.bmp in im_Metaplastic of test set\n",
      "Processed 167_04.bmp in im_Metaplastic of test set\n",
      "Processed 174_01.bmp in im_Metaplastic of test set\n",
      "Processed 176_01.bmp in im_Metaplastic of test set\n",
      "Processed 177_01.bmp in im_Metaplastic of test set\n",
      "Processed 178_01.bmp in im_Metaplastic of test set\n",
      "Processed 179_01.bmp in im_Metaplastic of test set\n",
      "Processed 184_01.bmp in im_Metaplastic of test set\n",
      "Processed 184_02.bmp in im_Metaplastic of test set\n",
      "Processed 184_03.bmp in im_Metaplastic of test set\n",
      "Processed 184_04.bmp in im_Metaplastic of test set\n",
      "Processed 185_02.bmp in im_Metaplastic of test set\n",
      "Processed 188_03.bmp in im_Metaplastic of test set\n",
      "Processed 201_02.bmp in im_Metaplastic of test set\n",
      "Processed 202_02.bmp in im_Metaplastic of test set\n",
      "Processed 202_05.bmp in im_Metaplastic of test set\n",
      "Processed 204_03.bmp in im_Metaplastic of test set\n",
      "Processed 205_01.bmp in im_Metaplastic of test set\n",
      "Processed 205_03.bmp in im_Metaplastic of test set\n",
      "Processed 207_02.bmp in im_Metaplastic of test set\n",
      "Processed 210_01.bmp in im_Metaplastic of test set\n",
      "Processed 211_01.bmp in im_Metaplastic of test set\n",
      "Processed 211_02.bmp in im_Metaplastic of test set\n",
      "Processed 213_01.bmp in im_Metaplastic of test set\n",
      "Processed 213_02.bmp in im_Metaplastic of test set\n",
      "Processed 216_01.bmp in im_Metaplastic of test set\n",
      "Processed 217_05.bmp in im_Metaplastic of test set\n",
      "Processed 217_07.bmp in im_Metaplastic of test set\n",
      "Processed 217_08.bmp in im_Metaplastic of test set\n",
      "Processed 218_01.bmp in im_Metaplastic of test set\n",
      "Processed 221_04.bmp in im_Metaplastic of test set\n",
      "Processed 224_01.bmp in im_Metaplastic of test set\n",
      "Processed 225_03.bmp in im_Metaplastic of test set\n",
      "Processed 228_01.bmp in im_Metaplastic of test set\n",
      "Processed 228_02.bmp in im_Metaplastic of test set\n",
      "Processed 236_02.bmp in im_Metaplastic of test set\n",
      "Processed 238_02.bmp in im_Metaplastic of test set\n",
      "Processed 239_02.bmp in im_Metaplastic of test set\n",
      "Processed 246_02.bmp in im_Metaplastic of test set\n",
      "Processed 250_01.bmp in im_Metaplastic of test set\n",
      "Processed 250_02.bmp in im_Metaplastic of test set\n",
      "Processed 254_04.bmp in im_Metaplastic of test set\n",
      "Processed 258_01.bmp in im_Metaplastic of test set\n",
      "Processed 259_01.bmp in im_Metaplastic of test set\n",
      "Processed 259_03.bmp in im_Metaplastic of test set\n",
      "Processed 261_03.bmp in im_Metaplastic of test set\n",
      "Processed 261_05.bmp in im_Metaplastic of test set\n",
      "Processed 261_10.bmp in im_Metaplastic of test set\n",
      "Processed 262_01.bmp in im_Metaplastic of test set\n",
      "Processed 262_02.bmp in im_Metaplastic of test set\n",
      "Processed 268_01.bmp in im_Metaplastic of test set\n",
      "Processed 270_02.bmp in im_Metaplastic of test set\n",
      "Processed 271_01.bmp in im_Metaplastic of test set\n",
      "Processed 001_11.bmp in im_Parabasal of test set\n",
      "Processed 003_07.bmp in im_Parabasal of test set\n",
      "Processed 003_13.bmp in im_Parabasal of test set\n",
      "Processed 004_01.bmp in im_Parabasal of test set\n",
      "Processed 004_02.bmp in im_Parabasal of test set\n",
      "Processed 004_04.bmp in im_Parabasal of test set\n",
      "Processed 004_10.bmp in im_Parabasal of test set\n",
      "Processed 006_01.bmp in im_Parabasal of test set\n",
      "Processed 006_06.bmp in im_Parabasal of test set\n",
      "Processed 008_08.bmp in im_Parabasal of test set\n",
      "Processed 008_09.bmp in im_Parabasal of test set\n",
      "Processed 008_10.bmp in im_Parabasal of test set\n",
      "Processed 010_02.bmp in im_Parabasal of test set\n",
      "Processed 010_03.bmp in im_Parabasal of test set\n",
      "Processed 010_04.bmp in im_Parabasal of test set\n",
      "Processed 010_07.bmp in im_Parabasal of test set\n",
      "Processed 010_10.bmp in im_Parabasal of test set\n",
      "Processed 011_02.bmp in im_Parabasal of test set\n",
      "Processed 011_13.bmp in im_Parabasal of test set\n",
      "Processed 014_02.bmp in im_Parabasal of test set\n",
      "Processed 015_01.bmp in im_Parabasal of test set\n",
      "Processed 015_03.bmp in im_Parabasal of test set\n",
      "Processed 017_02.bmp in im_Parabasal of test set\n",
      "Processed 022_01.bmp in im_Parabasal of test set\n",
      "Processed 022_07.bmp in im_Parabasal of test set\n",
      "Processed 026_01.bmp in im_Parabasal of test set\n",
      "Processed 026_05.bmp in im_Parabasal of test set\n",
      "Processed 027_01.bmp in im_Parabasal of test set\n",
      "Processed 028_05.bmp in im_Parabasal of test set\n",
      "Processed 029_01.bmp in im_Parabasal of test set\n",
      "Processed 029_04.bmp in im_Parabasal of test set\n",
      "Processed 030_01.bmp in im_Parabasal of test set\n",
      "Processed 031_08.bmp in im_Parabasal of test set\n",
      "Processed 032_02.bmp in im_Parabasal of test set\n",
      "Processed 032_03.bmp in im_Parabasal of test set\n",
      "Processed 032_07.bmp in im_Parabasal of test set\n",
      "Processed 034_03.bmp in im_Parabasal of test set\n",
      "Processed 037_02.bmp in im_Parabasal of test set\n",
      "Processed 037_07.bmp in im_Parabasal of test set\n",
      "Processed 042_08.bmp in im_Parabasal of test set\n",
      "Processed 042_12.bmp in im_Parabasal of test set\n",
      "Processed 043_01.bmp in im_Parabasal of test set\n",
      "Processed 044_01.bmp in im_Parabasal of test set\n",
      "Processed 047_03.bmp in im_Parabasal of test set\n",
      "Processed 047_06.bmp in im_Parabasal of test set\n",
      "Processed 047_07.bmp in im_Parabasal of test set\n",
      "Processed 048_04.bmp in im_Parabasal of test set\n",
      "Processed 048_05.bmp in im_Parabasal of test set\n",
      "Processed 049_04.bmp in im_Parabasal of test set\n",
      "Processed 049_10.bmp in im_Parabasal of test set\n",
      "Processed 051_03.bmp in im_Parabasal of test set\n",
      "Processed 052_01.bmp in im_Parabasal of test set\n",
      "Processed 053_03.bmp in im_Parabasal of test set\n",
      "Processed 053_07.bmp in im_Parabasal of test set\n",
      "Processed 057_04.bmp in im_Parabasal of test set\n",
      "Processed 057_08.bmp in im_Parabasal of test set\n",
      "Processed 058_01.bmp in im_Parabasal of test set\n",
      "Processed 060_07.bmp in im_Parabasal of test set\n",
      "Processed 061_01.bmp in im_Parabasal of test set\n",
      "Processed 062_01.bmp in im_Parabasal of test set\n",
      "Processed 062_07.bmp in im_Parabasal of test set\n",
      "Processed 062_09.bmp in im_Parabasal of test set\n",
      "Processed 062_10.bmp in im_Parabasal of test set\n",
      "Processed 063_01.bmp in im_Parabasal of test set\n",
      "Processed 063_03.bmp in im_Parabasal of test set\n",
      "Processed 065_07.bmp in im_Parabasal of test set\n",
      "Processed 067_05.bmp in im_Parabasal of test set\n",
      "Processed 069_03.bmp in im_Parabasal of test set\n",
      "Processed 069_07.bmp in im_Parabasal of test set\n",
      "Processed 071_07.bmp in im_Parabasal of test set\n",
      "Processed 071_08.bmp in im_Parabasal of test set\n",
      "Processed 072_01.bmp in im_Parabasal of test set\n",
      "Processed 072_05.bmp in im_Parabasal of test set\n",
      "Processed 072_07.bmp in im_Parabasal of test set\n",
      "Processed 073_01.bmp in im_Parabasal of test set\n",
      "Processed 075_01.bmp in im_Parabasal of test set\n",
      "Processed 075_02.bmp in im_Parabasal of test set\n",
      "Processed 075_07.bmp in im_Parabasal of test set\n",
      "Processed 075_13.bmp in im_Parabasal of test set\n",
      "Processed 077_02.bmp in im_Parabasal of test set\n",
      "Processed 078_07.bmp in im_Parabasal of test set\n",
      "Processed 081_03.bmp in im_Parabasal of test set\n",
      "Processed 081_06.bmp in im_Parabasal of test set\n",
      "Processed 082_01.bmp in im_Parabasal of test set\n",
      "Processed 083_01.bmp in im_Parabasal of test set\n",
      "Processed 083_02.bmp in im_Parabasal of test set\n",
      "Processed 083_03.bmp in im_Parabasal of test set\n",
      "Processed 085_01.bmp in im_Parabasal of test set\n",
      "Processed 090_01.bmp in im_Parabasal of test set\n",
      "Processed 093_03.bmp in im_Parabasal of test set\n",
      "Processed 093_13.bmp in im_Parabasal of test set\n",
      "Processed 093_15.bmp in im_Parabasal of test set\n",
      "Processed 093_18.bmp in im_Parabasal of test set\n",
      "Processed 093_24.bmp in im_Parabasal of test set\n",
      "Processed 093_26.bmp in im_Parabasal of test set\n",
      "Processed 094_02.bmp in im_Parabasal of test set\n",
      "Processed 094_03.bmp in im_Parabasal of test set\n",
      "Processed 095_02.bmp in im_Parabasal of test set\n",
      "Processed 095_17.bmp in im_Parabasal of test set\n",
      "Processed 096_05.bmp in im_Parabasal of test set\n",
      "Processed 096_07.bmp in im_Parabasal of test set\n",
      "Processed 097_06.bmp in im_Parabasal of test set\n",
      "Processed 098_05.bmp in im_Parabasal of test set\n",
      "Processed 098_08.bmp in im_Parabasal of test set\n",
      "Processed 099_12.bmp in im_Parabasal of test set\n",
      "Processed 100_08.bmp in im_Parabasal of test set\n",
      "Processed 100_10.bmp in im_Parabasal of test set\n",
      "Processed 100_12.bmp in im_Parabasal of test set\n",
      "Processed 101_02.bmp in im_Parabasal of test set\n",
      "Processed 101_06.bmp in im_Parabasal of test set\n",
      "Processed 102_03.bmp in im_Parabasal of test set\n",
      "Processed 102_05.bmp in im_Parabasal of test set\n",
      "Processed 102_16.bmp in im_Parabasal of test set\n",
      "Processed 106_03.bmp in im_Parabasal of test set\n",
      "Processed 106_07.bmp in im_Parabasal of test set\n",
      "Processed 106_09.bmp in im_Parabasal of test set\n",
      "Processed 107_03.bmp in im_Parabasal of test set\n",
      "Processed 107_04.bmp in im_Parabasal of test set\n",
      "Processed 108_02.bmp in im_Parabasal of test set\n",
      "Processed 002_01.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 002_06.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 004_10.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 005_04.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 005_05.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 006_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 007_01.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 007_11.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 007_16.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 008_09.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 009_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 009_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 009_04.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 009_09.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 010_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 010_04.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 010_05.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 010_11.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 011_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 013_04.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 013_05.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 015_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 015_04.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 017_06.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 018_01.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 018_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 020_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 021_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 021_09.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 026_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 027_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 027_07.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 028_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 028_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 028_04.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 028_07.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 029_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 030_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 032_01.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 032_06.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 032_09.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 033_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 034_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 035_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 035_04.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 036_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 036_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 043_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 043_04.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 044_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 045_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 046_10.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 050_04.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 051_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 053_07.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 054_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 055_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 057_08.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 059_04.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 061_05.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 061_06.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 062_01.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 062_06.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 063_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 065_05.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 066_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 066_04.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 066_06.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 066_09.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 068_05.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 070_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 071_01.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 071_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 075_01.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 076_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 078_05.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 080_01.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 080_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 080_07.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 080_09.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 081_05.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 081_06.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 082_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 082_07.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 083_07.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 092_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 094_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 094_06.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 096_05.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 097_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 097_07.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 097_09.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 098_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 098_07.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 099_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 099_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 103_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 104_05.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 104_06.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 106_07.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 107_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 109_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 111_04.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 112_07.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 113_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 114_04.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 118_04.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 118_07.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 118_10.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 119_05.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 119_07.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 119_09.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 120_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 120_04.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 121_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 122_01.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 122_04.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 123_05.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 124_01.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 124_07.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 124_08.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 125_05.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 126_02.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 126_03.bmp in im_Superficial-Intermediate of test set\n",
      "Processed 126_04.bmp in im_Superficial-Intermediate of test set\n",
      "All images have been processed and saved in the output folder.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "def constant_padding(image, size):\n",
    "    top = (size - image.shape[0]) // 2\n",
    "    bottom = size - image.shape[0] - top\n",
    "    left = (size - image.shape[1]) // 2\n",
    "    right = size - image.shape[1] - left\n",
    "    padded_image = cv2.copyMakeBorder(image, \n",
    "                                      top=top, \n",
    "                                      bottom=bottom, \n",
    "                                      left=left, \n",
    "                                      right=right, \n",
    "                                      borderType=cv2.BORDER_CONSTANT, \n",
    "                                      value=[0, 0, 0])\n",
    "    return padded_image\n",
    "\n",
    "base_input_folder = 'D:/ViuMe_Paper/sipakmed_split'\n",
    "base_output_folder = 'D:/ViuMe_Paper/sipakmed_padding'\n",
    "\n",
    "desired_size = 600\n",
    "\n",
    "for split in ['train', 'val', 'test']:\n",
    "    split_input_folder = os.path.join(base_input_folder, split)\n",
    "    split_output_folder = os.path.join(base_output_folder, split)\n",
    "\n",
    "    for category in os.listdir(split_input_folder):\n",
    "        category_input_folder = os.path.join(split_input_folder, category)\n",
    "        category_output_folder = os.path.join(split_output_folder, category)\n",
    "        os.makedirs(category_output_folder, exist_ok=True)\n",
    "\n",
    "        for filename in os.listdir(category_input_folder):\n",
    "            if filename.endswith('.bmp'):\n",
    "                input_image_path = os.path.join(category_input_folder, filename)\n",
    "\n",
    "                image = cv2.imread(input_image_path)\n",
    "\n",
    "                padded_image = constant_padding(image, desired_size)\n",
    "\n",
    "                output_image_path = os.path.join(category_output_folder, filename)\n",
    "                cv2.imwrite(output_image_path, padded_image)\n",
    "\n",
    "                print(f'Processed {filename} in {category} of {split} set')\n",
    "\n",
    "print('All images have been processed and saved in the output folder.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2832 images belonging to 5 classes.\n",
      "Found 608 images belonging to 5 classes.\n",
      "Found 609 images belonging to 5 classes.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# Data preprocessing\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True\n",
    ")\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    'D:/ViuMe_Paper/sipakmed_padding/train',\n",
    "    target_size=(600, 600),\n",
    "    batch_size=32,\n",
    "    class_mode='categorical'\n",
    ")\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "    'D:/ViuMe_Paper/sipakmed_padding/val',\n",
    "    target_size=(600, 600),\n",
    "    batch_size=32,\n",
    "    class_mode='categorical'\n",
    ")\n",
    "\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "    'D:/ViuMe_Paper/sipakmed_padding/test',\n",
    "    target_size=(600, 600),\n",
    "    batch_size=32,\n",
    "    class_mode='categorical',\n",
    "    shuffle=False \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'im_Dyskeratotic': 0,\n",
       " 'im_Koilocytotic': 1,\n",
       " 'im_Metaplastic': 2,\n",
       " 'im_Parabasal': 3,\n",
       " 'im_Superficial-Intermediate': 4}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_generator.class_indices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VGG16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "88/88 [==============================] - 91s 973ms/step - loss: 1.8552 - accuracy: 0.4154 - val_loss: 2.8344 - val_accuracy: 0.2467\n",
      "Epoch 2/50\n",
      "88/88 [==============================] - 76s 861ms/step - loss: 1.2527 - accuracy: 0.5804 - val_loss: 1.1610 - val_accuracy: 0.5378\n",
      "Epoch 3/50\n",
      "88/88 [==============================] - 76s 862ms/step - loss: 1.0838 - accuracy: 0.6414 - val_loss: 0.7171 - val_accuracy: 0.7336\n",
      "Epoch 4/50\n",
      "88/88 [==============================] - 76s 858ms/step - loss: 0.9412 - accuracy: 0.6832 - val_loss: 0.5861 - val_accuracy: 0.7714\n",
      "Epoch 5/50\n",
      "88/88 [==============================] - 76s 857ms/step - loss: 0.8950 - accuracy: 0.7054 - val_loss: 0.5318 - val_accuracy: 0.8026\n",
      "Epoch 6/50\n",
      "88/88 [==============================] - 75s 855ms/step - loss: 0.8668 - accuracy: 0.7107 - val_loss: 0.5185 - val_accuracy: 0.8076\n",
      "Epoch 7/50\n",
      "88/88 [==============================] - 76s 857ms/step - loss: 0.8501 - accuracy: 0.7250 - val_loss: 0.4657 - val_accuracy: 0.8289\n",
      "Epoch 8/50\n",
      "88/88 [==============================] - 75s 855ms/step - loss: 0.7806 - accuracy: 0.7364 - val_loss: 0.4598 - val_accuracy: 0.8322\n",
      "Epoch 9/50\n",
      "88/88 [==============================] - 75s 855ms/step - loss: 0.7507 - accuracy: 0.7507 - val_loss: 0.4536 - val_accuracy: 0.8339\n",
      "Epoch 10/50\n",
      "88/88 [==============================] - 76s 863ms/step - loss: 0.7869 - accuracy: 0.7329 - val_loss: 0.4314 - val_accuracy: 0.8388\n",
      "Epoch 11/50\n",
      "88/88 [==============================] - 77s 872ms/step - loss: 0.7548 - accuracy: 0.7454 - val_loss: 0.4357 - val_accuracy: 0.8421\n",
      "Epoch 12/50\n",
      "88/88 [==============================] - 77s 873ms/step - loss: 0.7380 - accuracy: 0.7514 - val_loss: 0.4401 - val_accuracy: 0.8470\n",
      "Epoch 13/50\n",
      "88/88 [==============================] - 77s 872ms/step - loss: 0.6841 - accuracy: 0.7632 - val_loss: 0.4309 - val_accuracy: 0.8520\n",
      "Epoch 14/50\n",
      "88/88 [==============================] - 77s 874ms/step - loss: 0.6857 - accuracy: 0.7754 - val_loss: 0.4290 - val_accuracy: 0.8553\n",
      "Epoch 15/50\n",
      "88/88 [==============================] - 77s 872ms/step - loss: 0.6969 - accuracy: 0.7596 - val_loss: 0.4080 - val_accuracy: 0.8503\n",
      "Epoch 16/50\n",
      "88/88 [==============================] - 77s 874ms/step - loss: 0.6501 - accuracy: 0.7814 - val_loss: 0.4031 - val_accuracy: 0.8602\n",
      "Epoch 17/50\n",
      "88/88 [==============================] - 77s 872ms/step - loss: 0.6492 - accuracy: 0.7746 - val_loss: 0.3991 - val_accuracy: 0.8569\n",
      "Epoch 18/50\n",
      "88/88 [==============================] - 77s 872ms/step - loss: 0.6297 - accuracy: 0.7771 - val_loss: 0.3950 - val_accuracy: 0.8536\n",
      "Epoch 19/50\n",
      "88/88 [==============================] - 77s 878ms/step - loss: 0.6424 - accuracy: 0.7832 - val_loss: 0.3946 - val_accuracy: 0.8520\n",
      "Epoch 20/50\n",
      "88/88 [==============================] - 77s 873ms/step - loss: 0.6142 - accuracy: 0.7900 - val_loss: 0.3852 - val_accuracy: 0.8553\n",
      "Epoch 21/50\n",
      "88/88 [==============================] - 77s 873ms/step - loss: 0.6409 - accuracy: 0.7832 - val_loss: 0.3745 - val_accuracy: 0.8586\n",
      "Epoch 22/50\n",
      "88/88 [==============================] - 76s 866ms/step - loss: 0.6181 - accuracy: 0.7843 - val_loss: 0.3733 - val_accuracy: 0.8668\n",
      "Epoch 23/50\n",
      "88/88 [==============================] - 76s 863ms/step - loss: 0.6367 - accuracy: 0.7932 - val_loss: 0.3742 - val_accuracy: 0.8569\n",
      "Epoch 24/50\n",
      "88/88 [==============================] - 76s 861ms/step - loss: 0.6203 - accuracy: 0.7896 - val_loss: 0.3741 - val_accuracy: 0.8536\n",
      "Epoch 25/50\n",
      "88/88 [==============================] - 75s 854ms/step - loss: 0.6073 - accuracy: 0.7936 - val_loss: 0.3811 - val_accuracy: 0.8553\n",
      "Epoch 26/50\n",
      "88/88 [==============================] - 75s 854ms/step - loss: 0.5850 - accuracy: 0.8018 - val_loss: 0.3754 - val_accuracy: 0.8569\n",
      "Epoch 27/50\n",
      "88/88 [==============================] - 76s 863ms/step - loss: 0.6153 - accuracy: 0.7921 - val_loss: 0.3838 - val_accuracy: 0.8553\n",
      "Epoch 28/50\n",
      "88/88 [==============================] - 76s 862ms/step - loss: 0.5632 - accuracy: 0.8014 - val_loss: 0.3707 - val_accuracy: 0.8553\n",
      "Epoch 29/50\n",
      "88/88 [==============================] - 76s 862ms/step - loss: 0.5664 - accuracy: 0.7993 - val_loss: 0.3845 - val_accuracy: 0.8520\n",
      "Epoch 30/50\n",
      "88/88 [==============================] - 76s 862ms/step - loss: 0.5538 - accuracy: 0.7957 - val_loss: 0.3846 - val_accuracy: 0.8503\n",
      "Epoch 31/50\n",
      "88/88 [==============================] - 76s 862ms/step - loss: 0.5855 - accuracy: 0.7936 - val_loss: 0.3805 - val_accuracy: 0.8454\n",
      "Epoch 32/50\n",
      "88/88 [==============================] - 76s 862ms/step - loss: 0.5718 - accuracy: 0.7986 - val_loss: 0.3819 - val_accuracy: 0.8536\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from keras.models import Model\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from keras.layers import Dense, GlobalMaxPool2D, BatchNormalization, Dropout\n",
    "\n",
    "pre_trained_model = tf.keras.applications.VGG16(input_shape=(600, 600, 3), weights='imagenet', include_top=False)\n",
    "for layer in pre_trained_model.layers:\n",
    "  layer.trainable = False\n",
    "x = pre_trained_model.get_layer(\"block4_conv3\").output\n",
    "x = GlobalMaxPool2D()(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(1024, activation='relu', name=\"dense_1024\")(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(5, activation='softmax')(x)\n",
    "\n",
    "modelvgg16 = Model(pre_trained_model.input, x)\n",
    "\n",
    "modelvgg16.compile(optimizer=Adam(learning_rate=1e-4),\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Callback\n",
    "early_stopping = EarlyStopping(monitor='val_accuracy', \n",
    "                               patience=10, \n",
    "                               restore_best_weights=True)\n",
    "\n",
    "model_checkpoint = ModelCheckpoint('D:/ViuMe_Paper/best model padding/bestmodel_vgg16.h5', \n",
    "                                    monitor='val_accuracy', \n",
    "                                    mode='max',\n",
    "                                    save_best_only=True)\n",
    "# Train the model\n",
    "history = modelvgg16.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
    "    epochs=50,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
    "    callbacks=[early_stopping, model_checkpoint]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 4s 193ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "                          precision    recall  f1-score   support\n",
      "\n",
      "            Dyskeratotic     0.9304    0.8770    0.9030       122\n",
      "            Koilocytotic     0.8879    0.7661    0.8225       124\n",
      "             Metaplastic     0.8319    0.7899    0.8103       119\n",
      "               Parabasal     0.8540    0.9832    0.9141       119\n",
      "Superficial-Intermediate     0.8613    0.9440    0.9008       125\n",
      "\n",
      "                accuracy                         0.8719       609\n",
      "               macro avg     0.8731    0.8721    0.8701       609\n",
      "            weighted avg     0.8734    0.8719    0.8702       609\n",
      "\n",
      "\n",
      "Class: Dyskeratotic\n",
      "True Positives (TP): 107\n",
      "False Positives (FP): 8\n",
      "False Negatives (FN): 15\n",
      "True Negatives (TN): 479\n",
      "\n",
      "Class: Koilocytotic\n",
      "True Positives (TP): 95\n",
      "False Positives (FP): 12\n",
      "False Negatives (FN): 29\n",
      "True Negatives (TN): 473\n",
      "\n",
      "Class: Metaplastic\n",
      "True Positives (TP): 94\n",
      "False Positives (FP): 19\n",
      "False Negatives (FN): 25\n",
      "True Negatives (TN): 471\n",
      "\n",
      "Class: Parabasal\n",
      "True Positives (TP): 117\n",
      "False Positives (FP): 20\n",
      "False Negatives (FN): 2\n",
      "True Negatives (TN): 470\n",
      "\n",
      "Class: Superficial\n",
      "True Positives (TP): 118\n",
      "False Positives (FP): 19\n",
      "False Negatives (FN): 7\n",
      "True Negatives (TN): 465\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras.models import load_model\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Load the best model\n",
    "best_model_path = 'D:/ViuMe_Paper/best model padding/bestmodel_vgg16.h5'\n",
    "best_model = load_model(best_model_path)\n",
    "\n",
    "# Calculate predictions on testing set\n",
    "y_pred_prob = best_model.predict(test_generator)\n",
    "y_true = test_generator.classes\n",
    "\n",
    "# Convert probabilities to class labels\n",
    "y_pred = np.argmax(y_pred_prob, axis=1)\n",
    "\n",
    "# Calculate confusion matrix\n",
    "confusion_mat = confusion_matrix(y_true, y_pred)\n",
    "class_labels = [label.replace(\"im_\", \"\").replace(\"Superficial-Intermediate\", \"Superficial\") for label in test_generator.class_indices.keys()]\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(confusion_mat, annot=True, fmt='d', cmap='Blues', xticklabels=class_labels, yticklabels=class_labels)\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n",
    "\n",
    "# Calculate and print classification report\n",
    "target_names = ['Dyskeratotic', 'Koilocytotic', 'Metaplastic', 'Parabasal', 'Superficial-Intermediate']\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_true, y_pred, target_names=target_names, digits=4))\n",
    "\n",
    "# Detailed confusion matrix analysis for each class\n",
    "\n",
    "\n",
    "for i in range(len(class_labels)):\n",
    "    TP = confusion_mat[i, i]\n",
    "    FP = sum(confusion_mat[:, i]) - TP\n",
    "    FN = sum(confusion_mat[i, :]) - TP\n",
    "    TN = sum(sum(confusion_mat)) - TP - FP - FN\n",
    "\n",
    "    print(f\"\\nClass: {class_labels[i]}\")\n",
    "    print(f\"True Positives (TP): {TP}\")\n",
    "    print(f\"False Positives (FP): {FP}\")\n",
    "    print(f\"False Negatives (FN): {FN}\")\n",
    "    print(f\"True Negatives (TN): {TN}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VGG19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "88/88 [==============================] - 111s 1s/step - loss: 1.4434 - accuracy: 0.3704 - val_loss: 2.6442 - val_accuracy: 0.2056\n",
      "Epoch 2/50\n",
      "88/88 [==============================] - 133s 2s/step - loss: 1.2557 - accuracy: 0.4582 - val_loss: 12.8941 - val_accuracy: 0.2056\n",
      "Epoch 3/50\n",
      "88/88 [==============================] - 77s 873ms/step - loss: 1.1560 - accuracy: 0.5057 - val_loss: 47.9520 - val_accuracy: 0.2056\n",
      "Epoch 4/50\n",
      "88/88 [==============================] - 77s 873ms/step - loss: 1.0866 - accuracy: 0.5321 - val_loss: 16.9796 - val_accuracy: 0.2056\n",
      "Epoch 5/50\n",
      "88/88 [==============================] - 77s 872ms/step - loss: 0.9858 - accuracy: 0.5768 - val_loss: 14.4994 - val_accuracy: 0.2056\n",
      "Epoch 6/50\n",
      "88/88 [==============================] - 77s 875ms/step - loss: 0.9496 - accuracy: 0.5950 - val_loss: 1.9275 - val_accuracy: 0.4424\n",
      "Epoch 7/50\n",
      "88/88 [==============================] - 77s 872ms/step - loss: 0.8725 - accuracy: 0.6332 - val_loss: 24.4015 - val_accuracy: 0.2056\n",
      "Epoch 8/50\n",
      "88/88 [==============================] - 77s 872ms/step - loss: 0.8423 - accuracy: 0.6621 - val_loss: 4.5903 - val_accuracy: 0.4194\n",
      "Epoch 9/50\n",
      "88/88 [==============================] - 77s 874ms/step - loss: 0.8154 - accuracy: 0.6768 - val_loss: 6.5650 - val_accuracy: 0.2072\n",
      "Epoch 10/50\n",
      "88/88 [==============================] - 77s 872ms/step - loss: 0.7175 - accuracy: 0.7314 - val_loss: 1.2480 - val_accuracy: 0.5148\n",
      "Epoch 11/50\n",
      "88/88 [==============================] - 77s 870ms/step - loss: 0.6293 - accuracy: 0.7596 - val_loss: 2.5673 - val_accuracy: 0.3750\n",
      "Epoch 12/50\n",
      "88/88 [==============================] - 77s 873ms/step - loss: 0.5853 - accuracy: 0.7868 - val_loss: 2.4334 - val_accuracy: 0.5362\n",
      "Epoch 13/50\n",
      "88/88 [==============================] - 77s 874ms/step - loss: 0.5403 - accuracy: 0.8125 - val_loss: 2.9274 - val_accuracy: 0.4704\n",
      "Epoch 14/50\n",
      "88/88 [==============================] - 77s 871ms/step - loss: 0.5718 - accuracy: 0.8050 - val_loss: 2.1456 - val_accuracy: 0.4622\n",
      "Epoch 15/50\n",
      "88/88 [==============================] - 77s 875ms/step - loss: 0.4611 - accuracy: 0.8429 - val_loss: 1.0808 - val_accuracy: 0.6168\n",
      "Epoch 16/50\n",
      "88/88 [==============================] - 77s 873ms/step - loss: 0.4637 - accuracy: 0.8450 - val_loss: 5.7148 - val_accuracy: 0.2944\n",
      "Epoch 17/50\n",
      "88/88 [==============================] - 77s 872ms/step - loss: 0.4212 - accuracy: 0.8596 - val_loss: 1.8466 - val_accuracy: 0.5280\n",
      "Epoch 18/50\n",
      "88/88 [==============================] - 77s 873ms/step - loss: 0.3903 - accuracy: 0.8668 - val_loss: 1.0376 - val_accuracy: 0.6924\n",
      "Epoch 19/50\n",
      "88/88 [==============================] - 77s 873ms/step - loss: 0.4009 - accuracy: 0.8639 - val_loss: 0.4974 - val_accuracy: 0.8174\n",
      "Epoch 20/50\n",
      "88/88 [==============================] - 77s 876ms/step - loss: 0.3991 - accuracy: 0.8711 - val_loss: 0.6825 - val_accuracy: 0.7615\n",
      "Epoch 21/50\n",
      "88/88 [==============================] - 77s 873ms/step - loss: 0.3461 - accuracy: 0.8786 - val_loss: 2.9263 - val_accuracy: 0.4852\n",
      "Epoch 22/50\n",
      "88/88 [==============================] - 77s 874ms/step - loss: 0.3545 - accuracy: 0.8768 - val_loss: 10.2436 - val_accuracy: 0.3520\n",
      "Epoch 23/50\n",
      "88/88 [==============================] - 77s 875ms/step - loss: 0.3654 - accuracy: 0.8786 - val_loss: 1.3283 - val_accuracy: 0.6562\n",
      "Epoch 24/50\n",
      "88/88 [==============================] - 77s 877ms/step - loss: 0.3164 - accuracy: 0.8914 - val_loss: 2.4438 - val_accuracy: 0.3997\n",
      "Epoch 25/50\n",
      "88/88 [==============================] - 77s 874ms/step - loss: 0.3443 - accuracy: 0.8893 - val_loss: 0.4213 - val_accuracy: 0.8750\n",
      "Epoch 26/50\n",
      "88/88 [==============================] - 77s 871ms/step - loss: 0.3226 - accuracy: 0.8943 - val_loss: 0.3525 - val_accuracy: 0.8931\n",
      "Epoch 27/50\n",
      "88/88 [==============================] - 76s 866ms/step - loss: 0.2997 - accuracy: 0.8996 - val_loss: 0.3405 - val_accuracy: 0.8865\n",
      "Epoch 28/50\n",
      "88/88 [==============================] - 77s 867ms/step - loss: 0.3051 - accuracy: 0.9007 - val_loss: 0.7223 - val_accuracy: 0.8076\n",
      "Epoch 29/50\n",
      "88/88 [==============================] - 77s 872ms/step - loss: 0.2594 - accuracy: 0.9150 - val_loss: 0.8158 - val_accuracy: 0.7385\n",
      "Epoch 30/50\n",
      "88/88 [==============================] - 77s 873ms/step - loss: 0.2520 - accuracy: 0.9175 - val_loss: 3.4056 - val_accuracy: 0.4391\n",
      "Epoch 31/50\n",
      "88/88 [==============================] - 77s 873ms/step - loss: 0.2808 - accuracy: 0.9129 - val_loss: 1.1222 - val_accuracy: 0.6497\n",
      "Epoch 32/50\n",
      "88/88 [==============================] - 77s 876ms/step - loss: 0.2584 - accuracy: 0.9143 - val_loss: 0.3717 - val_accuracy: 0.8651\n",
      "Epoch 33/50\n",
      "88/88 [==============================] - 77s 874ms/step - loss: 0.2205 - accuracy: 0.9286 - val_loss: 1.2499 - val_accuracy: 0.6760\n",
      "Epoch 34/50\n",
      "88/88 [==============================] - 77s 873ms/step - loss: 0.2363 - accuracy: 0.9236 - val_loss: 1.0400 - val_accuracy: 0.7566\n",
      "Epoch 35/50\n",
      "88/88 [==============================] - 77s 876ms/step - loss: 0.2082 - accuracy: 0.9329 - val_loss: 2.0122 - val_accuracy: 0.6053\n",
      "Epoch 36/50\n",
      "88/88 [==============================] - 78s 885ms/step - loss: 0.2358 - accuracy: 0.9271 - val_loss: 0.5705 - val_accuracy: 0.8289\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from keras.models import Model\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from keras.layers import Dense, GlobalAveragePooling2D, BatchNormalization, Dropout\n",
    "\n",
    "pre_trained_model = tf.keras.applications.VGG19(input_shape=(600, 600, 3), weights='imagenet', include_top=False)\n",
    "for layer in pre_trained_model.layers[:-10]:\n",
    "    layer.trainable = False\n",
    "x = pre_trained_model.output\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(256, activation='relu')(x)\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(5, activation='softmax')(x)\n",
    "\n",
    "modelvgg19 = Model(pre_trained_model.input, x)\n",
    "\n",
    "modelvgg19.compile(optimizer=Adam(learning_rate=1e-4),\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Callback\n",
    "early_stopping = EarlyStopping(monitor='val_accuracy', \n",
    "                               patience=10, \n",
    "                               restore_best_weights=True)\n",
    "\n",
    "model_checkpoint = ModelCheckpoint('D:/ViuMe_Paper/best model padding/bestmodel_vgg19.h5', \n",
    "                                    monitor='val_accuracy', \n",
    "                                    mode='max',\n",
    "                                    save_best_only=True)\n",
    "\n",
    "# Train the model\n",
    "history = modelvgg19.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
    "    epochs=50,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
    "    callbacks=[early_stopping, model_checkpoint]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 5s 213ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "                          precision    recall  f1-score   support\n",
      "\n",
      "            Dyskeratotic     0.8682    0.9180    0.8924       122\n",
      "            Koilocytotic     0.8060    0.8710    0.8372       124\n",
      "             Metaplastic     0.8689    0.8908    0.8797       119\n",
      "               Parabasal     1.0000    0.7479    0.8558       119\n",
      "Superficial-Intermediate     0.9111    0.9840    0.9462       125\n",
      "\n",
      "                accuracy                         0.8834       609\n",
      "               macro avg     0.8908    0.8823    0.8822       609\n",
      "            weighted avg     0.8902    0.8834    0.8826       609\n",
      "\n",
      "\n",
      "Class: Dyskeratotic\n",
      "True Positives (TP): 112\n",
      "False Positives (FP): 17\n",
      "False Negatives (FN): 10\n",
      "True Negatives (TN): 470\n",
      "\n",
      "Class: Koilocytotic\n",
      "True Positives (TP): 108\n",
      "False Positives (FP): 26\n",
      "False Negatives (FN): 16\n",
      "True Negatives (TN): 459\n",
      "\n",
      "Class: Metaplastic\n",
      "True Positives (TP): 106\n",
      "False Positives (FP): 16\n",
      "False Negatives (FN): 13\n",
      "True Negatives (TN): 474\n",
      "\n",
      "Class: Parabasal\n",
      "True Positives (TP): 89\n",
      "False Positives (FP): 0\n",
      "False Negatives (FN): 30\n",
      "True Negatives (TN): 490\n",
      "\n",
      "Class: Superficial\n",
      "True Positives (TP): 123\n",
      "False Positives (FP): 12\n",
      "False Negatives (FN): 2\n",
      "True Negatives (TN): 472\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras.models import load_model\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Load the best model\n",
    "best_model_path = 'D:/ViuMe_Paper/best model padding/bestmodel_vgg19.h5' \n",
    "best_model = load_model(best_model_path)\n",
    "\n",
    "# Calculate predictions on testing set\n",
    "y_pred_prob = best_model.predict(test_generator)\n",
    "y_true = test_generator.classes\n",
    "\n",
    "# Convert probabilities to class labels\n",
    "y_pred = np.argmax(y_pred_prob, axis=1)\n",
    "\n",
    "# Calculate confusion matrix\n",
    "confusion_mat = confusion_matrix(y_true, y_pred)\n",
    "class_labels = [label.replace(\"im_\", \"\").replace(\"Superficial-Intermediate\", \"Superficial\") for label in test_generator.class_indices.keys()]\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(confusion_mat, annot=True, fmt='d', cmap='Blues', xticklabels=class_labels, yticklabels=class_labels)\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n",
    "\n",
    "# Calculate and print classification report\n",
    "target_names = ['Dyskeratotic', 'Koilocytotic', 'Metaplastic', 'Parabasal', 'Superficial-Intermediate']\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_true, y_pred, target_names=target_names, digits=4))\n",
    "\n",
    "# Detailed confusion matrix analysis for each class\n",
    "\n",
    "\n",
    "for i in range(len(class_labels)):\n",
    "    TP = confusion_mat[i, i]\n",
    "    FP = sum(confusion_mat[:, i]) - TP\n",
    "    FN = sum(confusion_mat[i, :]) - TP\n",
    "    TN = sum(sum(confusion_mat)) - TP - FP - FN\n",
    "\n",
    "    print(f\"\\nClass: {class_labels[i]}\")\n",
    "    print(f\"True Positives (TP): {TP}\")\n",
    "    print(f\"False Positives (FP): {FP}\")\n",
    "    print(f\"False Negatives (FN): {FN}\")\n",
    "    print(f\"True Negatives (TN): {TN}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# InceptionV3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "88/88 [==============================] - 83s 904ms/step - loss: 1.3701 - accuracy: 0.5329 - val_loss: 1.1542 - val_accuracy: 0.4507\n",
      "Epoch 2/50\n",
      "88/88 [==============================] - 76s 864ms/step - loss: 0.9363 - accuracy: 0.6825 - val_loss: 0.7015 - val_accuracy: 0.7878\n",
      "Epoch 3/50\n",
      "88/88 [==============================] - 76s 860ms/step - loss: 0.8142 - accuracy: 0.7211 - val_loss: 0.5387 - val_accuracy: 0.8207\n",
      "Epoch 4/50\n",
      "88/88 [==============================] - 76s 860ms/step - loss: 0.7128 - accuracy: 0.7500 - val_loss: 0.4519 - val_accuracy: 0.8438\n",
      "Epoch 5/50\n",
      "88/88 [==============================] - 76s 860ms/step - loss: 0.6921 - accuracy: 0.7650 - val_loss: 0.3931 - val_accuracy: 0.8569\n",
      "Epoch 6/50\n",
      "88/88 [==============================] - 75s 855ms/step - loss: 0.6244 - accuracy: 0.7807 - val_loss: 0.3611 - val_accuracy: 0.8602\n",
      "Epoch 7/50\n",
      "88/88 [==============================] - 75s 855ms/step - loss: 0.6600 - accuracy: 0.7829 - val_loss: 0.3409 - val_accuracy: 0.8701\n",
      "Epoch 8/50\n",
      "88/88 [==============================] - 75s 855ms/step - loss: 0.5975 - accuracy: 0.7936 - val_loss: 0.3252 - val_accuracy: 0.8799\n",
      "Epoch 9/50\n",
      "88/88 [==============================] - 75s 855ms/step - loss: 0.5535 - accuracy: 0.8068 - val_loss: 0.3084 - val_accuracy: 0.8882\n",
      "Epoch 10/50\n",
      "88/88 [==============================] - 75s 851ms/step - loss: 0.5541 - accuracy: 0.8075 - val_loss: 0.3080 - val_accuracy: 0.8865\n",
      "Epoch 11/50\n",
      "88/88 [==============================] - 75s 855ms/step - loss: 0.5467 - accuracy: 0.8061 - val_loss: 0.2966 - val_accuracy: 0.9030\n",
      "Epoch 12/50\n",
      "88/88 [==============================] - 75s 851ms/step - loss: 0.5839 - accuracy: 0.8036 - val_loss: 0.2970 - val_accuracy: 0.9013\n",
      "Epoch 13/50\n",
      "88/88 [==============================] - 75s 856ms/step - loss: 0.5218 - accuracy: 0.8189 - val_loss: 0.2884 - val_accuracy: 0.9013\n",
      "Epoch 14/50\n",
      "88/88 [==============================] - 76s 859ms/step - loss: 0.5193 - accuracy: 0.8211 - val_loss: 0.2888 - val_accuracy: 0.9062\n",
      "Epoch 15/50\n",
      "88/88 [==============================] - 75s 856ms/step - loss: 0.5053 - accuracy: 0.8261 - val_loss: 0.2824 - val_accuracy: 0.9013\n",
      "Epoch 16/50\n",
      "88/88 [==============================] - 75s 852ms/step - loss: 0.4853 - accuracy: 0.8296 - val_loss: 0.2889 - val_accuracy: 0.8997\n",
      "Epoch 17/50\n",
      "88/88 [==============================] - 76s 859ms/step - loss: 0.5032 - accuracy: 0.8311 - val_loss: 0.2819 - val_accuracy: 0.9013\n",
      "Epoch 18/50\n",
      "88/88 [==============================] - 76s 865ms/step - loss: 0.5168 - accuracy: 0.8161 - val_loss: 0.2783 - val_accuracy: 0.8997\n",
      "Epoch 19/50\n",
      "88/88 [==============================] - 75s 857ms/step - loss: 0.4563 - accuracy: 0.8468 - val_loss: 0.2755 - val_accuracy: 0.8997\n",
      "Epoch 20/50\n",
      "88/88 [==============================] - 76s 860ms/step - loss: 0.4680 - accuracy: 0.8314 - val_loss: 0.2753 - val_accuracy: 0.9128\n",
      "Epoch 21/50\n",
      "88/88 [==============================] - 76s 859ms/step - loss: 0.4615 - accuracy: 0.8439 - val_loss: 0.2737 - val_accuracy: 0.9112\n",
      "Epoch 22/50\n",
      "88/88 [==============================] - 75s 853ms/step - loss: 0.4769 - accuracy: 0.8329 - val_loss: 0.2697 - val_accuracy: 0.9046\n",
      "Epoch 23/50\n",
      "88/88 [==============================] - 75s 853ms/step - loss: 0.4669 - accuracy: 0.8254 - val_loss: 0.2743 - val_accuracy: 0.9128\n",
      "Epoch 24/50\n",
      "88/88 [==============================] - 75s 854ms/step - loss: 0.4656 - accuracy: 0.8389 - val_loss: 0.2744 - val_accuracy: 0.9013\n",
      "Epoch 25/50\n",
      "88/88 [==============================] - 75s 854ms/step - loss: 0.4349 - accuracy: 0.8421 - val_loss: 0.2749 - val_accuracy: 0.9079\n",
      "Epoch 26/50\n",
      "88/88 [==============================] - 75s 855ms/step - loss: 0.4213 - accuracy: 0.8604 - val_loss: 0.2713 - val_accuracy: 0.8997\n",
      "Epoch 27/50\n",
      "88/88 [==============================] - 75s 853ms/step - loss: 0.4288 - accuracy: 0.8504 - val_loss: 0.2705 - val_accuracy: 0.9013\n",
      "Epoch 28/50\n",
      "88/88 [==============================] - 75s 853ms/step - loss: 0.4477 - accuracy: 0.8379 - val_loss: 0.2697 - val_accuracy: 0.9062\n",
      "Epoch 29/50\n",
      "88/88 [==============================] - 75s 854ms/step - loss: 0.4328 - accuracy: 0.8482 - val_loss: 0.2658 - val_accuracy: 0.9030\n",
      "Epoch 30/50\n",
      "88/88 [==============================] - 75s 856ms/step - loss: 0.4237 - accuracy: 0.8471 - val_loss: 0.2612 - val_accuracy: 0.9112\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from keras.models import Model\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from keras.layers import Dense, GlobalMaxPool2D, BatchNormalization, Dropout\n",
    "\n",
    "pre_trained_model = tf.keras.applications.InceptionV3(input_shape=(600, 600, 3), weights='imagenet', include_top=False)\n",
    "for layer in pre_trained_model.layers:\n",
    "    layer.trainable = False\n",
    "x = pre_trained_model.get_layer(\"mixed7\").output\n",
    "x = GlobalMaxPool2D()(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(1024, activation='relu', name=\"dense_1024\")(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(5, activation='softmax')(x)\n",
    "\n",
    "modelInceptionV3 = Model(pre_trained_model.input, x)\n",
    "\n",
    "modelInceptionV3.compile(optimizer=Adam(learning_rate=1e-4),\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Callback\n",
    "early_stopping = EarlyStopping(monitor='val_accuracy', \n",
    "                               patience=10, \n",
    "                               restore_best_weights=True)\n",
    "\n",
    "model_checkpoint = ModelCheckpoint('D:/ViuMe_Paper/best model padding/bestmodel_InceptionV3.h5', \n",
    "                                    monitor='val_accuracy', \n",
    "                                    mode='max',\n",
    "                                    save_best_only=True)\n",
    "# Train the model\n",
    "history = modelInceptionV3.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
    "    epochs=50,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
    "    callbacks=[early_stopping, model_checkpoint]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 3s 109ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "                          precision    recall  f1-score   support\n",
      "\n",
      "            Dyskeratotic     0.9187    0.9262    0.9224       122\n",
      "            Koilocytotic     0.8305    0.7903    0.8099       124\n",
      "             Metaplastic     0.8374    0.8655    0.8512       119\n",
      "               Parabasal     0.9573    0.9412    0.9492       119\n",
      "Superficial-Intermediate     0.9688    0.9920    0.9802       125\n",
      "\n",
      "                accuracy                         0.9031       609\n",
      "               macro avg     0.9025    0.9031    0.9026       609\n",
      "            weighted avg     0.9027    0.9031    0.9027       609\n",
      "\n",
      "\n",
      "Class: Dyskeratotic\n",
      "True Positives (TP): 113\n",
      "False Positives (FP): 10\n",
      "False Negatives (FN): 9\n",
      "True Negatives (TN): 477\n",
      "\n",
      "Class: Koilocytotic\n",
      "True Positives (TP): 98\n",
      "False Positives (FP): 20\n",
      "False Negatives (FN): 26\n",
      "True Negatives (TN): 465\n",
      "\n",
      "Class: Metaplastic\n",
      "True Positives (TP): 103\n",
      "False Positives (FP): 20\n",
      "False Negatives (FN): 16\n",
      "True Negatives (TN): 470\n",
      "\n",
      "Class: Parabasal\n",
      "True Positives (TP): 112\n",
      "False Positives (FP): 5\n",
      "False Negatives (FN): 7\n",
      "True Negatives (TN): 485\n",
      "\n",
      "Class: Superficial\n",
      "True Positives (TP): 124\n",
      "False Positives (FP): 4\n",
      "False Negatives (FN): 1\n",
      "True Negatives (TN): 480\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras.models import load_model\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Load the best model\n",
    "best_model_path = 'D:/ViuMe_Paper/best model padding/bestmodel_InceptionV3.h5'  \n",
    "best_model = load_model(best_model_path)\n",
    "\n",
    "# Calculate predictions on testing set\n",
    "y_pred_prob = best_model.predict(test_generator)\n",
    "y_true = test_generator.classes\n",
    "\n",
    "# Convert probabilities to class labels\n",
    "y_pred = np.argmax(y_pred_prob, axis=1)\n",
    "\n",
    "# Calculate confusion matrix\n",
    "confusion_mat = confusion_matrix(y_true, y_pred)\n",
    "class_labels = [label.replace(\"im_\", \"\").replace(\"Superficial-Intermediate\", \"Superficial\") for label in test_generator.class_indices.keys()]\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(confusion_mat, annot=True, fmt='d', cmap='Blues', xticklabels=class_labels, yticklabels=class_labels)\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n",
    "\n",
    "# Calculate and print classification report\n",
    "target_names = ['Dyskeratotic', 'Koilocytotic', 'Metaplastic', 'Parabasal', 'Superficial-Intermediate']\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_true, y_pred, target_names=target_names, digits=4))\n",
    "\n",
    "# Detailed confusion matrix analysis for each class\n",
    "\n",
    "\n",
    "for i in range(len(class_labels)):\n",
    "    TP = confusion_mat[i, i]\n",
    "    FP = sum(confusion_mat[:, i]) - TP\n",
    "    FN = sum(confusion_mat[i, :]) - TP\n",
    "    TN = sum(sum(confusion_mat)) - TP - FP - FN\n",
    "\n",
    "    print(f\"\\nClass: {class_labels[i]}\")\n",
    "    print(f\"True Positives (TP): {TP}\")\n",
    "    print(f\"False Positives (FP): {FP}\")\n",
    "    print(f\"False Negatives (FN): {FN}\")\n",
    "    print(f\"True Negatives (TN): {TN}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DenseNet201"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "88/88 [==============================] - 97s 1s/step - loss: 0.7774 - accuracy: 0.7446 - val_loss: 1.4347 - val_accuracy: 0.4688\n",
      "Epoch 2/50\n",
      "88/88 [==============================] - 81s 918ms/step - loss: 0.3928 - accuracy: 0.8675 - val_loss: 1.3026 - val_accuracy: 0.4145\n",
      "Epoch 3/50\n",
      "88/88 [==============================] - 79s 897ms/step - loss: 0.3071 - accuracy: 0.9000 - val_loss: 1.0742 - val_accuracy: 0.5724\n",
      "Epoch 4/50\n",
      "88/88 [==============================] - 79s 897ms/step - loss: 0.2792 - accuracy: 0.9043 - val_loss: 0.7434 - val_accuracy: 0.7434\n",
      "Epoch 5/50\n",
      "88/88 [==============================] - 89s 1s/step - loss: 0.2641 - accuracy: 0.9093 - val_loss: 0.4696 - val_accuracy: 0.8684\n",
      "Epoch 6/50\n",
      "88/88 [==============================] - 143s 2s/step - loss: 0.2191 - accuracy: 0.9236 - val_loss: 0.2407 - val_accuracy: 0.9227\n",
      "Epoch 7/50\n",
      "88/88 [==============================] - 89s 1s/step - loss: 0.2153 - accuracy: 0.9196 - val_loss: 0.1900 - val_accuracy: 0.9309\n",
      "Epoch 8/50\n",
      "88/88 [==============================] - 92s 1s/step - loss: 0.2042 - accuracy: 0.9232 - val_loss: 0.1913 - val_accuracy: 0.9276\n",
      "Epoch 9/50\n",
      "88/88 [==============================] - 78s 885ms/step - loss: 0.2007 - accuracy: 0.9243 - val_loss: 0.1849 - val_accuracy: 0.9359\n",
      "Epoch 10/50\n",
      "88/88 [==============================] - 77s 877ms/step - loss: 0.1825 - accuracy: 0.9346 - val_loss: 0.1816 - val_accuracy: 0.9326\n",
      "Epoch 11/50\n",
      "88/88 [==============================] - 78s 887ms/step - loss: 0.1651 - accuracy: 0.9436 - val_loss: 0.1741 - val_accuracy: 0.9408\n",
      "Epoch 12/50\n",
      "88/88 [==============================] - 77s 877ms/step - loss: 0.1617 - accuracy: 0.9450 - val_loss: 0.1916 - val_accuracy: 0.9326\n",
      "Epoch 13/50\n",
      "88/88 [==============================] - 77s 877ms/step - loss: 0.1538 - accuracy: 0.9464 - val_loss: 0.1953 - val_accuracy: 0.9326\n",
      "Epoch 14/50\n",
      "88/88 [==============================] - 77s 878ms/step - loss: 0.1688 - accuracy: 0.9407 - val_loss: 0.1802 - val_accuracy: 0.9326\n",
      "Epoch 15/50\n",
      "88/88 [==============================] - 78s 880ms/step - loss: 0.1559 - accuracy: 0.9479 - val_loss: 0.1864 - val_accuracy: 0.9342\n",
      "Epoch 16/50\n",
      "88/88 [==============================] - 78s 880ms/step - loss: 0.1399 - accuracy: 0.9443 - val_loss: 0.1765 - val_accuracy: 0.9326\n",
      "Epoch 17/50\n",
      "88/88 [==============================] - 78s 879ms/step - loss: 0.1291 - accuracy: 0.9550 - val_loss: 0.1693 - val_accuracy: 0.9359\n",
      "Epoch 18/50\n",
      "88/88 [==============================] - 77s 878ms/step - loss: 0.1501 - accuracy: 0.9479 - val_loss: 0.1725 - val_accuracy: 0.9326\n",
      "Epoch 19/50\n",
      "88/88 [==============================] - 77s 878ms/step - loss: 0.1460 - accuracy: 0.9468 - val_loss: 0.1746 - val_accuracy: 0.9375\n",
      "Epoch 20/50\n",
      "88/88 [==============================] - 78s 883ms/step - loss: 0.1338 - accuracy: 0.9493 - val_loss: 0.1684 - val_accuracy: 0.9424\n",
      "Epoch 21/50\n",
      "88/88 [==============================] - 77s 877ms/step - loss: 0.1283 - accuracy: 0.9536 - val_loss: 0.1737 - val_accuracy: 0.9342\n",
      "Epoch 22/50\n",
      "88/88 [==============================] - 77s 878ms/step - loss: 0.1306 - accuracy: 0.9543 - val_loss: 0.1685 - val_accuracy: 0.9424\n",
      "Epoch 23/50\n",
      "88/88 [==============================] - 78s 879ms/step - loss: 0.1310 - accuracy: 0.9521 - val_loss: 0.1615 - val_accuracy: 0.9375\n",
      "Epoch 24/50\n",
      "88/88 [==============================] - 78s 879ms/step - loss: 0.1388 - accuracy: 0.9471 - val_loss: 0.1587 - val_accuracy: 0.9359\n",
      "Epoch 25/50\n",
      "88/88 [==============================] - 77s 878ms/step - loss: 0.1256 - accuracy: 0.9568 - val_loss: 0.1561 - val_accuracy: 0.9408\n",
      "Epoch 26/50\n",
      "88/88 [==============================] - 78s 884ms/step - loss: 0.1188 - accuracy: 0.9546 - val_loss: 0.1572 - val_accuracy: 0.9441\n",
      "Epoch 27/50\n",
      "88/88 [==============================] - 77s 878ms/step - loss: 0.1162 - accuracy: 0.9579 - val_loss: 0.1481 - val_accuracy: 0.9441\n",
      "Epoch 28/50\n",
      "88/88 [==============================] - 77s 878ms/step - loss: 0.1378 - accuracy: 0.9543 - val_loss: 0.1766 - val_accuracy: 0.9342\n",
      "Epoch 29/50\n",
      "88/88 [==============================] - 78s 884ms/step - loss: 0.0900 - accuracy: 0.9668 - val_loss: 0.1553 - val_accuracy: 0.9474\n",
      "Epoch 30/50\n",
      "88/88 [==============================] - 78s 886ms/step - loss: 0.1032 - accuracy: 0.9636 - val_loss: 0.1513 - val_accuracy: 0.9523\n",
      "Epoch 31/50\n",
      "88/88 [==============================] - 78s 880ms/step - loss: 0.0924 - accuracy: 0.9632 - val_loss: 0.1561 - val_accuracy: 0.9441\n",
      "Epoch 32/50\n",
      "88/88 [==============================] - 77s 877ms/step - loss: 0.0884 - accuracy: 0.9700 - val_loss: 0.1512 - val_accuracy: 0.9490\n",
      "Epoch 33/50\n",
      "88/88 [==============================] - 78s 881ms/step - loss: 0.1085 - accuracy: 0.9650 - val_loss: 0.1925 - val_accuracy: 0.9326\n",
      "Epoch 34/50\n",
      "88/88 [==============================] - 78s 879ms/step - loss: 0.0931 - accuracy: 0.9679 - val_loss: 0.1622 - val_accuracy: 0.9408\n",
      "Epoch 35/50\n",
      "88/88 [==============================] - 77s 877ms/step - loss: 0.0947 - accuracy: 0.9654 - val_loss: 0.1557 - val_accuracy: 0.9474\n",
      "Epoch 36/50\n",
      "88/88 [==============================] - 77s 878ms/step - loss: 0.0941 - accuracy: 0.9636 - val_loss: 0.1501 - val_accuracy: 0.9523\n",
      "Epoch 37/50\n",
      "88/88 [==============================] - 77s 877ms/step - loss: 0.0956 - accuracy: 0.9682 - val_loss: 0.1488 - val_accuracy: 0.9474\n",
      "Epoch 38/50\n",
      "88/88 [==============================] - 78s 879ms/step - loss: 0.0919 - accuracy: 0.9671 - val_loss: 0.1488 - val_accuracy: 0.9507\n",
      "Epoch 39/50\n",
      "88/88 [==============================] - 78s 882ms/step - loss: 0.0936 - accuracy: 0.9643 - val_loss: 0.1592 - val_accuracy: 0.9490\n",
      "Epoch 40/50\n",
      "88/88 [==============================] - 78s 886ms/step - loss: 0.1000 - accuracy: 0.9671 - val_loss: 0.1623 - val_accuracy: 0.9457\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from keras.models import Model\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from keras.layers import Dense, GlobalMaxPooling2D, BatchNormalization, Dropout\n",
    "\n",
    "pre_trained_model = tf.keras.applications.DenseNet201(input_shape=(600, 600, 3), weights='imagenet', include_top=False)\n",
    "for layer in pre_trained_model.layers:\n",
    "  layer.trainable = False\n",
    "# Fine-tune beberapa lapisan terakhir\n",
    "for layer in pre_trained_model.layers[-10:]:\n",
    "    layer.trainable = True\n",
    "x = pre_trained_model.get_layer(\"conv5_block16_concat\").output\n",
    "x = GlobalMaxPooling2D()(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.4)(x)  \n",
    "x = Dense(2048, activation='relu', name=\"dense_2048\")(x)  \n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.4)(x)\n",
    "x = Dense(5, activation='softmax')(x)\n",
    "\n",
    "modelDenseNet201 = Model(pre_trained_model.input, x)\n",
    "\n",
    "modelDenseNet201.compile(optimizer=Adam(learning_rate=1e-4),\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Callback\n",
    "early_stopping = EarlyStopping(monitor='val_accuracy', \n",
    "                               patience=10, \n",
    "                               restore_best_weights=True)\n",
    "\n",
    "model_checkpoint = ModelCheckpoint('D:/ViuMe_Paper/best model padding/bestmodel_DenseNet201.h5', \n",
    "                                    monitor='val_accuracy', \n",
    "                                    mode='max',\n",
    "                                    save_best_only=True)\n",
    "# Train the model\n",
    "history = modelDenseNet201.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
    "    epochs=50,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
    "    callbacks=[early_stopping, model_checkpoint]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 5s 209ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "                          precision    recall  f1-score   support\n",
      "\n",
      "            Dyskeratotic     0.9360    0.9590    0.9474       122\n",
      "            Koilocytotic     0.9224    0.8629    0.8917       124\n",
      "             Metaplastic     0.9412    0.9412    0.9412       119\n",
      "               Parabasal     1.0000    1.0000    1.0000       119\n",
      "Superficial-Intermediate     0.9615    1.0000    0.9804       125\n",
      "\n",
      "                accuracy                         0.9524       609\n",
      "               macro avg     0.9522    0.9526    0.9521       609\n",
      "            weighted avg     0.9520    0.9524    0.9519       609\n",
      "\n",
      "\n",
      "Class: Dyskeratotic\n",
      "True Positives (TP): 117\n",
      "False Positives (FP): 8\n",
      "False Negatives (FN): 5\n",
      "True Negatives (TN): 479\n",
      "\n",
      "Class: Koilocytotic\n",
      "True Positives (TP): 107\n",
      "False Positives (FP): 9\n",
      "False Negatives (FN): 17\n",
      "True Negatives (TN): 476\n",
      "\n",
      "Class: Metaplastic\n",
      "True Positives (TP): 112\n",
      "False Positives (FP): 7\n",
      "False Negatives (FN): 7\n",
      "True Negatives (TN): 483\n",
      "\n",
      "Class: Parabasal\n",
      "True Positives (TP): 119\n",
      "False Positives (FP): 0\n",
      "False Negatives (FN): 0\n",
      "True Negatives (TN): 490\n",
      "\n",
      "Class: Superficial\n",
      "True Positives (TP): 125\n",
      "False Positives (FP): 5\n",
      "False Negatives (FN): 0\n",
      "True Negatives (TN): 479\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras.models import load_model\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Load the best model\n",
    "best_model_path = 'D:/ViuMe_Paper/best model padding/bestmodel_DenseNet201.h5' \n",
    "best_model = load_model(best_model_path)\n",
    "\n",
    "# Calculate predictions on testing set\n",
    "y_pred_prob = best_model.predict(test_generator)\n",
    "y_true = test_generator.classes\n",
    "\n",
    "# Convert probabilities to class labels\n",
    "y_pred = np.argmax(y_pred_prob, axis=1)\n",
    "\n",
    "# Calculate confusion matrix\n",
    "confusion_mat = confusion_matrix(y_true, y_pred)\n",
    "class_labels = [label.replace(\"im_\", \"\").replace(\"Superficial-Intermediate\", \"Superficial\") for label in test_generator.class_indices.keys()]\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(confusion_mat, annot=True, fmt='d', cmap='Blues', xticklabels=class_labels, yticklabels=class_labels)\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n",
    "\n",
    "# Calculate and print classification report\n",
    "target_names = ['Dyskeratotic', 'Koilocytotic', 'Metaplastic', 'Parabasal', 'Superficial-Intermediate']\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_true, y_pred, target_names=target_names, digits=4))\n",
    "\n",
    "# Detailed confusion matrix analysis for each class\n",
    "\n",
    "\n",
    "for i in range(len(class_labels)):\n",
    "    TP = confusion_mat[i, i]\n",
    "    FP = sum(confusion_mat[:, i]) - TP\n",
    "    FN = sum(confusion_mat[i, :]) - TP\n",
    "    TN = sum(sum(confusion_mat)) - TP - FP - FN\n",
    "\n",
    "    print(f\"\\nClass: {class_labels[i]}\")\n",
    "    print(f\"True Positives (TP): {TP}\")\n",
    "    print(f\"False Positives (FP): {FP}\")\n",
    "    print(f\"False Negatives (FN): {FN}\")\n",
    "    print(f\"True Negatives (TN): {TN}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XceptionNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "88/88 [==============================] - 93s 992ms/step - loss: 1.1864 - accuracy: 0.6057 - val_loss: 1.6422 - val_accuracy: 0.2039\n",
      "Epoch 2/50\n",
      "88/88 [==============================] - 76s 859ms/step - loss: 0.7074 - accuracy: 0.7489 - val_loss: 1.5930 - val_accuracy: 0.2993\n",
      "Epoch 3/50\n",
      "88/88 [==============================] - 75s 852ms/step - loss: 0.5788 - accuracy: 0.7929 - val_loss: 1.3512 - val_accuracy: 0.3849\n",
      "Epoch 4/50\n",
      "88/88 [==============================] - 75s 851ms/step - loss: 0.5603 - accuracy: 0.8143 - val_loss: 0.9971 - val_accuracy: 0.5855\n",
      "Epoch 5/50\n",
      "88/88 [==============================] - 75s 848ms/step - loss: 0.4769 - accuracy: 0.8314 - val_loss: 0.6885 - val_accuracy: 0.7188\n",
      "Epoch 6/50\n",
      "88/88 [==============================] - 81s 924ms/step - loss: 0.4673 - accuracy: 0.8450 - val_loss: 0.3679 - val_accuracy: 0.8602\n",
      "Epoch 7/50\n",
      "88/88 [==============================] - 78s 880ms/step - loss: 0.4522 - accuracy: 0.8364 - val_loss: 0.2904 - val_accuracy: 0.8882\n",
      "Epoch 8/50\n",
      "88/88 [==============================] - 75s 851ms/step - loss: 0.4173 - accuracy: 0.8493 - val_loss: 0.2429 - val_accuracy: 0.9128\n",
      "Epoch 9/50\n",
      "88/88 [==============================] - 75s 851ms/step - loss: 0.3816 - accuracy: 0.8636 - val_loss: 0.2383 - val_accuracy: 0.9211\n",
      "Epoch 10/50\n",
      "88/88 [==============================] - 1242s 14s/step - loss: 0.3947 - accuracy: 0.8607 - val_loss: 0.2383 - val_accuracy: 0.9161\n",
      "Epoch 11/50\n",
      "88/88 [==============================] - 73s 833ms/step - loss: 0.3698 - accuracy: 0.8711 - val_loss: 0.2305 - val_accuracy: 0.9178\n",
      "Epoch 12/50\n",
      "88/88 [==============================] - 74s 839ms/step - loss: 0.4095 - accuracy: 0.8582 - val_loss: 0.2245 - val_accuracy: 0.9161\n",
      "Epoch 13/50\n",
      "88/88 [==============================] - 75s 847ms/step - loss: 0.3715 - accuracy: 0.8739 - val_loss: 0.2248 - val_accuracy: 0.9211\n",
      "Epoch 14/50\n",
      "88/88 [==============================] - 75s 846ms/step - loss: 0.3554 - accuracy: 0.8704 - val_loss: 0.2303 - val_accuracy: 0.9178\n",
      "Epoch 15/50\n",
      "88/88 [==============================] - 74s 843ms/step - loss: 0.3628 - accuracy: 0.8746 - val_loss: 0.2321 - val_accuracy: 0.9194\n",
      "Epoch 16/50\n",
      "88/88 [==============================] - 75s 847ms/step - loss: 0.3573 - accuracy: 0.8732 - val_loss: 0.2257 - val_accuracy: 0.9227\n",
      "Epoch 17/50\n",
      "88/88 [==============================] - 74s 844ms/step - loss: 0.3586 - accuracy: 0.8732 - val_loss: 0.2148 - val_accuracy: 0.9227\n",
      "Epoch 18/50\n",
      "88/88 [==============================] - 76s 857ms/step - loss: 0.3356 - accuracy: 0.8782 - val_loss: 0.2199 - val_accuracy: 0.9243\n",
      "Epoch 19/50\n",
      "88/88 [==============================] - 75s 856ms/step - loss: 0.3146 - accuracy: 0.8839 - val_loss: 0.2193 - val_accuracy: 0.9243\n",
      "Epoch 20/50\n",
      "88/88 [==============================] - 76s 857ms/step - loss: 0.3242 - accuracy: 0.8871 - val_loss: 0.2234 - val_accuracy: 0.9211\n",
      "Epoch 21/50\n",
      "88/88 [==============================] - 76s 863ms/step - loss: 0.3085 - accuracy: 0.8911 - val_loss: 0.2204 - val_accuracy: 0.9293\n",
      "Epoch 22/50\n",
      "88/88 [==============================] - 76s 866ms/step - loss: 0.3309 - accuracy: 0.8836 - val_loss: 0.2207 - val_accuracy: 0.9260\n",
      "Epoch 23/50\n",
      "88/88 [==============================] - 76s 858ms/step - loss: 0.3164 - accuracy: 0.8900 - val_loss: 0.2255 - val_accuracy: 0.9293\n",
      "Epoch 24/50\n",
      "88/88 [==============================] - 76s 859ms/step - loss: 0.2762 - accuracy: 0.9014 - val_loss: 0.2299 - val_accuracy: 0.9276\n",
      "Epoch 25/50\n",
      "88/88 [==============================] - 76s 860ms/step - loss: 0.3123 - accuracy: 0.8864 - val_loss: 0.2232 - val_accuracy: 0.9227\n",
      "Epoch 26/50\n",
      "88/88 [==============================] - 76s 859ms/step - loss: 0.2876 - accuracy: 0.8925 - val_loss: 0.2274 - val_accuracy: 0.9293\n",
      "Epoch 27/50\n",
      "88/88 [==============================] - 76s 862ms/step - loss: 0.2780 - accuracy: 0.9021 - val_loss: 0.2205 - val_accuracy: 0.9309\n",
      "Epoch 28/50\n",
      "88/88 [==============================] - 76s 858ms/step - loss: 0.2774 - accuracy: 0.8996 - val_loss: 0.2201 - val_accuracy: 0.9276\n",
      "Epoch 29/50\n",
      "88/88 [==============================] - 76s 857ms/step - loss: 0.2850 - accuracy: 0.8957 - val_loss: 0.2168 - val_accuracy: 0.9276\n",
      "Epoch 30/50\n",
      "88/88 [==============================] - 76s 859ms/step - loss: 0.2839 - accuracy: 0.8950 - val_loss: 0.2331 - val_accuracy: 0.9243\n",
      "Epoch 31/50\n",
      "88/88 [==============================] - 75s 856ms/step - loss: 0.2720 - accuracy: 0.8989 - val_loss: 0.2187 - val_accuracy: 0.9309\n",
      "Epoch 32/50\n",
      "88/88 [==============================] - 75s 857ms/step - loss: 0.2971 - accuracy: 0.8911 - val_loss: 0.2158 - val_accuracy: 0.9309\n",
      "Epoch 33/50\n",
      "88/88 [==============================] - 76s 857ms/step - loss: 0.2886 - accuracy: 0.9036 - val_loss: 0.2099 - val_accuracy: 0.9293\n",
      "Epoch 34/50\n",
      "88/88 [==============================] - 76s 857ms/step - loss: 0.2435 - accuracy: 0.9104 - val_loss: 0.2127 - val_accuracy: 0.9309\n",
      "Epoch 35/50\n",
      "88/88 [==============================] - 76s 859ms/step - loss: 0.2603 - accuracy: 0.9050 - val_loss: 0.2062 - val_accuracy: 0.9293\n",
      "Epoch 36/50\n",
      "88/88 [==============================] - 76s 860ms/step - loss: 0.2710 - accuracy: 0.9039 - val_loss: 0.2163 - val_accuracy: 0.9326\n",
      "Epoch 37/50\n",
      "88/88 [==============================] - 76s 859ms/step - loss: 0.2870 - accuracy: 0.8932 - val_loss: 0.2127 - val_accuracy: 0.9309\n",
      "Epoch 38/50\n",
      "88/88 [==============================] - 76s 859ms/step - loss: 0.2727 - accuracy: 0.9061 - val_loss: 0.2124 - val_accuracy: 0.9276\n",
      "Epoch 39/50\n",
      "88/88 [==============================] - 76s 858ms/step - loss: 0.2487 - accuracy: 0.9064 - val_loss: 0.2080 - val_accuracy: 0.9260\n",
      "Epoch 40/50\n",
      "88/88 [==============================] - 76s 861ms/step - loss: 0.2516 - accuracy: 0.9107 - val_loss: 0.2048 - val_accuracy: 0.9375\n",
      "Epoch 41/50\n",
      "88/88 [==============================] - 76s 857ms/step - loss: 0.2379 - accuracy: 0.9136 - val_loss: 0.2025 - val_accuracy: 0.9243\n",
      "Epoch 42/50\n",
      "88/88 [==============================] - 76s 865ms/step - loss: 0.2506 - accuracy: 0.9052 - val_loss: 0.2025 - val_accuracy: 0.9293\n",
      "Epoch 43/50\n",
      "88/88 [==============================] - 76s 859ms/step - loss: 0.2453 - accuracy: 0.9114 - val_loss: 0.2011 - val_accuracy: 0.9342\n",
      "Epoch 44/50\n",
      "88/88 [==============================] - 76s 860ms/step - loss: 0.2456 - accuracy: 0.9121 - val_loss: 0.1984 - val_accuracy: 0.9326\n",
      "Epoch 45/50\n",
      "88/88 [==============================] - 76s 858ms/step - loss: 0.2193 - accuracy: 0.9182 - val_loss: 0.2045 - val_accuracy: 0.9342\n",
      "Epoch 46/50\n",
      "88/88 [==============================] - 76s 860ms/step - loss: 0.2274 - accuracy: 0.9171 - val_loss: 0.2074 - val_accuracy: 0.9359\n",
      "Epoch 47/50\n",
      "88/88 [==============================] - 76s 861ms/step - loss: 0.2224 - accuracy: 0.9186 - val_loss: 0.2088 - val_accuracy: 0.9276\n",
      "Epoch 48/50\n",
      "88/88 [==============================] - 76s 860ms/step - loss: 0.2387 - accuracy: 0.9136 - val_loss: 0.1937 - val_accuracy: 0.9309\n",
      "Epoch 49/50\n",
      "88/88 [==============================] - 76s 860ms/step - loss: 0.2348 - accuracy: 0.9154 - val_loss: 0.2128 - val_accuracy: 0.9243\n",
      "Epoch 50/50\n",
      "88/88 [==============================] - 76s 861ms/step - loss: 0.2223 - accuracy: 0.9186 - val_loss: 0.2079 - val_accuracy: 0.9326\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from keras.models import Model\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from keras.layers import Dense, GlobalAveragePooling2D, BatchNormalization, Dropout\n",
    "\n",
    "pre_trained_model = tf.keras.applications.Xception(input_shape=(600, 600, 3), weights='imagenet', include_top=False)\n",
    "for layer in pre_trained_model.layers:\n",
    "  layer.trainable = False\n",
    "x = pre_trained_model.get_layer(\"block14_sepconv1_act\").output\n",
    "x = GlobalAveragePooling2D()(x)  \n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.4)(x) \n",
    "x = Dense(1024, activation='relu', name=\"dense_1024\")(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.4)(x)  \n",
    "x = Dense(512, activation='relu')(x)  \n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.4)(x)  \n",
    "x = Dense(5, activation='softmax')(x)\n",
    "\n",
    "modelXception = Model(pre_trained_model.input, x)\n",
    "\n",
    "\n",
    "modelXception.compile(optimizer=Adam(learning_rate=1e-4),\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Callback\n",
    "early_stopping = EarlyStopping(monitor='val_accuracy', \n",
    "                               patience=10, \n",
    "                               restore_best_weights=True)\n",
    "\n",
    "model_checkpoint = ModelCheckpoint('D:/ViuMe_Paper/best model padding/bestmodel_Xception.h5', \n",
    "                                    monitor='val_accuracy', \n",
    "                                    mode='max',\n",
    "                                    save_best_only=True)\n",
    "# Train the model\n",
    "history = modelXception.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
    "    epochs=50,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
    "    callbacks=[early_stopping, model_checkpoint]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 3s 133ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "                          precision    recall  f1-score   support\n",
      "\n",
      "            Dyskeratotic     0.9500    0.9344    0.9421       122\n",
      "            Koilocytotic     0.9016    0.8871    0.8943       124\n",
      "             Metaplastic     0.9167    0.9244    0.9205       119\n",
      "               Parabasal     0.9500    0.9580    0.9540       119\n",
      "Superficial-Intermediate     0.9764    0.9920    0.9841       125\n",
      "\n",
      "                accuracy                         0.9392       609\n",
      "               macro avg     0.9389    0.9392    0.9390       609\n",
      "            weighted avg     0.9391    0.9392    0.9391       609\n",
      "\n",
      "\n",
      "Class: Dyskeratotic\n",
      "True Positives (TP): 114\n",
      "False Positives (FP): 6\n",
      "False Negatives (FN): 8\n",
      "True Negatives (TN): 481\n",
      "\n",
      "Class: Koilocytotic\n",
      "True Positives (TP): 110\n",
      "False Positives (FP): 12\n",
      "False Negatives (FN): 14\n",
      "True Negatives (TN): 473\n",
      "\n",
      "Class: Metaplastic\n",
      "True Positives (TP): 110\n",
      "False Positives (FP): 10\n",
      "False Negatives (FN): 9\n",
      "True Negatives (TN): 480\n",
      "\n",
      "Class: Parabasal\n",
      "True Positives (TP): 114\n",
      "False Positives (FP): 6\n",
      "False Negatives (FN): 5\n",
      "True Negatives (TN): 484\n",
      "\n",
      "Class: Superficial\n",
      "True Positives (TP): 124\n",
      "False Positives (FP): 3\n",
      "False Negatives (FN): 1\n",
      "True Negatives (TN): 481\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras.models import load_model\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Load the best model\n",
    "best_model_path = 'D:/ViuMe_Paper/best model padding/bestmodel_Xception.h5'  # Sesuaikan dengan path model Anda\n",
    "best_model = load_model(best_model_path)\n",
    "\n",
    "# Calculate predictions on testing set\n",
    "y_pred_prob = best_model.predict(test_generator)\n",
    "y_true = test_generator.classes\n",
    "\n",
    "# Convert probabilities to class labels\n",
    "y_pred = np.argmax(y_pred_prob, axis=1)\n",
    "\n",
    "# Calculate confusion matrix\n",
    "confusion_mat = confusion_matrix(y_true, y_pred)\n",
    "class_labels = [label.replace(\"im_\", \"\").replace(\"Superficial-Intermediate\", \"Superficial\") for label in test_generator.class_indices.keys()]\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(confusion_mat, annot=True, fmt='d', cmap='Blues', xticklabels=class_labels, yticklabels=class_labels)\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n",
    "\n",
    "# Calculate and print classification report\n",
    "target_names = ['Dyskeratotic', 'Koilocytotic', 'Metaplastic', 'Parabasal', 'Superficial-Intermediate']\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_true, y_pred, target_names=target_names, digits=4))\n",
    "\n",
    "# Detailed confusion matrix analysis for each class\n",
    "for i in range(len(class_labels)):\n",
    "    TP = confusion_mat[i, i]\n",
    "    FP = sum(confusion_mat[:, i]) - TP\n",
    "    FN = sum(confusion_mat[i, :]) - TP\n",
    "    TN = sum(sum(confusion_mat)) - TP - FP - FN\n",
    "\n",
    "    print(f\"\\nClass: {class_labels[i]}\")\n",
    "    print(f\"True Positives (TP): {TP}\")\n",
    "    print(f\"False Positives (FP): {FP}\")\n",
    "    print(f\"False Negatives (FN): {FN}\")\n",
    "    print(f\"True Negatives (TN): {TN}\")"
   ]
  }
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