Cancer Classification and Prediction

A Django-based web application for cancer classification and prediction using deep learning models. This project implements multiple neural network models for various cancer detection and organ classification tasks.

📋 Table of Contents

• Overview
• Features
• Models
• Prerequisites
• Installation
• Model Setup
• Usage
• Project Structure
• Technologies Used
• Contributing
• License

🎯 Overview

This application leverages deep learning models to assist in cancer classification and prediction tasks. The system provides a user-friendly web interface built with Django, allowing users to interact with pre-trained neural network models for various diagnostic purposes.

✨ Features

• Multi-Model Support: Includes multiple trained models for different cancer classification tasks
• Web-Based Interface: Easy-to-use Django web application
• Real-time Predictions: Get instant predictions from uploaded medical data
• Organ Classification: Specialized model for organ classification
• Scalable Architecture: Built on Django framework for easy expansion

🤖 Models

The project includes four pre-trained deep learning models:

1. best_model_2.h5 - Primary cancer classification model
2. UC.h5 - Specialized classification model
3. my_gall.h5 - Gallery-based classification model
4. organclassifier.h5 - Organ classification model

All models are built using Keras/TensorFlow and saved in HDF5 format.

📦 Prerequisites

• Python 3.7 or higher
• pip (Python package installer)
• Virtual environment (recommended)

🚀 Installation

1. Clone the Repository

git clone https://github.com/kartik912/cancerClassificationAndPrediction.git
cd cancerClassificationAndPrediction

2. Create Virtual Environment (Optional but Recommended)

# Windows
python -m venv venv
venv\Scripts\activate

# Linux/Mac
python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

📥 Model Setup

The trained models need to be downloaded separately due to their large file sizes. Follow these steps:

1. Download Models

Download all four model files from the following Google Drive links:

• best_model_2.h5: Download Link
• UC.h5: Download Link
• my_gall.h5: Download Link
• organclassifier.h5: Download Link

2. Place Models in Correct Directory

After downloading, place all four .h5 model files in the model21 folder:

cancerClassificationAndPrediction/
└── model21/
    ├── best_model_2.h5
    ├── UC.h5
    ├── my_gall.h5
    └── organclassifier.h5

💻 Usage

Running the Development Server

1. Ensure all models are placed in the model21 folder
2. Navigate to the project directory
3. Run the Django development server:

python manage.py runserver

4. Open your web browser and navigate to:

http://127.0.0.1:8000/

Running on a Custom Port

python manage.py runserver 8080

Then access at: http://127.0.0.1:8080/

📁 Project Structure

cancerClassificationAndPrediction/
├── model21/                  # Directory for trained models
│   ├── best_model_2.h5
│   ├── UC.h5
│   ├── my_gall.h5
│   └── organclassifier.h5
├── manage.py                 # Django management script
├── requirements.txt          # Python dependencies
├── db.sqlite3               # SQLite database (created on first run)
└── [other Django app files]

🛠️ Technologies Used

• Backend Framework: Django
• Deep Learning: TensorFlow/Keras
• Model Format: HDF5 (.h5)
• Database: SQLite (default Django database)
• Python Libraries: Listed in requirements.txt

🔧 Common Issues and Troubleshooting

Models Not Found Error

Issue: Application crashes or throws "model not found" error
Solution: Ensure all four .h5 files are downloaded and placed in the model21 folder

Module Import Errors

Issue: Import errors when running the server
Solution: Make sure all dependencies are installed via pip install -r requirements.txt

Port Already in Use

Issue: Port 8000 is already occupied
Solution: Use a different port: python manage.py runserver 8080

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature/improvement)
3. Make your changes
4. Commit your changes (git commit -am 'Add new feature')
5. Push to the branch (git push origin feature/improvement)
6. Create a Pull Request

📧 Contact

For questions or support, please open an issue in the GitHub repository.

⚠️ Disclaimer

This application is for educational and research purposes only. It should not be used as a substitute for professional medical advice, diagnosis, or treatment. Always consult with qualified healthcare professionals for medical decisions.

🙏 Acknowledgments

• Thanks to all contributors and the open-source community
• Deep learning models trained on publicly available medical datasets

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Note: Make sure to keep your models and sensitive data secure. Never commit large model files directly to the repository.