PCOS Detection Model

This repository contains code to create a PCOS (Polycystic Ovary Syndrome) detection model using XGBoost and VGG16 pre-trained model.

Dataset used: PCOS DATASET

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Model Creation Steps:

1. Dataset Collection: Gathered a dataset containing images for PCOS detection. The dataset should be organized into 'train' and 'test' sets.

2. Preprocessing: Utilized OpenCV and NumPy to read, resize, and process images. Imported the necessary libraries including NumPy, Matplotlib, OpenCV, TensorFlow, XGBoost, and others.

3. Data Preprocessing: Defined functions for loading and preprocessing images from the 'train' and 'test' directories. Normalized pixel values of the images by dividing by 255.0 to scale them between 0 and 1.

4. Feature Extraction: Used the VGG16 pre-trained model as a feature extractor to obtain features from the images in both 'train' and 'test' datasets. Extracted features and reshaped them into a suitable format for training and testing the XGBoost classifier.

5. Model Training: Implemented an XGBoost classifier to train on the extracted features from the 'train' dataset. Encoded labels using LabelEncoder from the scikit-learn library to convert text labels to integer format. Trained the XGBoost model using the extracted features and their respective labels.

6. Model Evaluation: Evaluated the trained model using the 'test' dataset. Calculated accuracy and generated a confusion matrix to visualize the performance of the model. Produced a classification report to further assess the model's performance.

7. Model Saving:

   • Saved the trained XGBoost model using joblib for future use.
   • Prerequisites:
     • Python 3.x
     • Required libraries: NumPy, Matplotlib, OpenCV, TensorFlow, XGBoost, scikit-learn, Seaborn

8. Model Deployment

   • Deployed the model using Streamlit.
   • When a image is passed as a input to the streamlit app, the feature extraction of the image is done using the VGG16 model and the extracted features are passed to the XGBoost model for prediction.

Steps to Run the Code:

• Ensure all necessary libraries are installed by running:

    pip install -r requirements.txt.

• Prepare the dataset in the required directory structure ('train' and 'test' directories).
• Run the script to train the model:

    python main.py

• The trained model will be saved as 'xray.pkl'.
• Use the model for predictions or further analysis.
• To run the webapp, run the following command:

    streamlit run webapp.py

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Model Information:

• Model: XGBoost Classifier
• Base Model: VGG16 (pre-trained on ImageNet)
• Files Included: main.py: Contains the code to create and train the PCOS detection model.
• xray.pkl: The trained XGBoost model. requirements.txt: Lists the required Python libraries and their versions.
• webapp.py : Contains the code to deploy the model using streamlit.

Screenshots:

• Webapp:

  • Case "PCOS Positive":

  • Case "PCOS Negative":