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🩺 Chest Cancer Detection with Streamlit

Welcome to the Chest Cancer Detection project! This repository hosts a Streamlit web application designed to identify various types of lung cancer from chest X-ray images using a deep learning model.

🌟 Project Overview

Our application utilizes a pre-trained InceptionV3 model to provide predictions on uploaded chest X-ray images. This tool aims to assist in the early detection of lung cancer, enhancing diagnostic efficiency and accuracy.

Key Features

  • Real-Time Prediction: Upload an X-ray image and receive instant predictions.
  • User-Friendly Interface: Simple and intuitive design for seamless interaction.
  • Model Integration: Uses advanced deep learning techniques for accurate results.

Project Screenshot

🚀 Getting Started

To get a copy of this project up and running on your local machine, follow these steps.

Prerequisites

  • Python 3.7 or higher
  • Git
  • Git Large File Storage (LFS)

Installation Steps

  1. Clone the Repository

    Open your terminal or command prompt and run:

    git clone https://github.com/amitkumar2308/Cancer-detection-streamlit.git
cd Cancer-detection-streamlit

  2. Setup Virtual Environment

       python -m venv .venv
   source .venv/bin/activate  # On Windows use .venv\Scripts\activate

  3. Install Dependencies

     pip install -r requirements.txt

  4. For Uploading Large files

    Use Git LFS

  5. Run the application

    streamlit run model.py

🖼️ How It Works

  1. Upload an Image

    Click on the "Choose an image..." button to select a chest X-ray image in .png, .jpg, or .jpeg format.

  2. Receive Predictions

    Click "Predict" to analyze the image. The application will display the predicted type of lung cancer and provide confidence levels.

Example

  • Image Upload: [Include image upload example here]
  • Prediction Result: [Include prediction result example here]

🤝 Contributing

We welcome contributions to improve this project. To contribute:

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

🧑‍💻 Development Guidelines

  • Use black for code formatting.
  • Write tests for new features.
  • Ensure code is well-documented.

📜 License

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

📞 Contact

For questions or feedback, please contact: