Breast Cancer Detection Using Deep Learning

Table of Contents

1. Introduction
2. Dataset
3. Features
4. Technologies Used
5. Installation
6. Usage
7. Example Output
8. Contributing
9. License
10. Author

Introduction

Breast cancer is one of the most common types of cancer affecting women worldwide. Early detection and diagnosis are crucial for effective treatment and improving survival rates. This project aims to assist in the early detection of breast cancer using machine learning algorithms. By analyzing various features extracted from cell images, the model can classify tumors as benign or malignant with high accuracy. The goal is to provide a reliable tool that can aid medical professionals in making more informed decisions.

This project demonstrates the end-to-end process of building a machine learning model, from data preprocessing and feature selection to model training, evaluation, and deployment. Various machine learning algorithms are implemented and compared to identify the best-performing model for this classification task.

Dataset

The dataset used in this project is the Breast Cancer Wisconsin (Diagnostic) dataset. It consists of 569 samples, each with 30 features, including mean, standard error, and worst (mean of the three largest values) of ten real-valued features computed for each cell nucleus.

Features

• Data preprocessing and normalization
• Implementation of various machine learning models
• Evaluation of model performance
• Visualization of results

Technologies Used

• Python 3.x
• Jupyter Notebook
• Scikit-learn
• Pandas
• Numpy
• Matplotlib
• Seaborn

Installation

1. Clone the repository:

   git clone https://github.com/your-username/Breast-Cancer-Detection.git
   cd Breast-Cancer-Detection

2. Create a virtual environment:

   python -m venv venv

3. Activate the virtual environment:

   • On Windows:

     venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

4. Install the required packages:

   pip install -r requirements.txt

Usage

1. Open the Jupyter Notebook:

   jupyter notebook Breast_Cancer_Detection.ipynb

2. Run the cells in the notebook to preprocess the data, train the models, and evaluate the results.

Example Output

Here are some example outputs from the project: Accuracy of SVM model: 98.2% Confusion Matrix: [ [102 3] [2 63] ]

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

For any questions or suggestions, please contact:

• Harsh Singh: [email protected]
• GitHub: harshjuly12