README.md

metadata

title: MRI Brain Tumor Detection
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: docker
app_port: 7860
pinned: false

🧠 MRI Brain Tumor Detection System

Deep Learning application for automated brain tumor classification from MRI scans using a custom ResidualInceptionBlock CNN architecture.

🎯 Features

• 4-Class Classification: Glioma, Meningioma, Pituitary, No Tumor
• Real-time Inference: Fast predictions with confidence scores
• Modern UI: Clean, responsive React interface
• RESTful API: FastAPI backend with automatic documentation

🏗️ Architecture

• Frontend: React 18 + Vite
• Backend: FastAPI + PyTorch
• Model: Custom ResidualInceptionBlock CNN (50+ layers)
• Deployment: Docker + Hugging Face Spaces

🚀 Quick Start

Using the Deployed App

Simply visit the app URL and upload an MRI scan image to get instant predictions.

Local Development

1. Clone the repository

git clone <your-repo-url>
cd mri-diagnosis-app

2. Start with Docker Compose

docker-compose up --build

3. Access the application

• Frontend: http://localhost:3000
• API Docs: http://localhost:8000/docs

Manual Setup

Backend:

cd backend
pip install -r requirements.txt
uvicorn app.main:app --reload

Frontend:

cd frontend
npm install
npm run dev

📋 API Endpoints

• POST /api/predict - Upload MRI image for prediction
• GET /health - Health check endpoint
• GET /docs - Interactive API documentation

🎨 Usage

1. Upload an MRI brain scan (PNG, JPG, JPEG)
2. Click "Run Diagnosis"
3. View prediction with confidence score

📊 Model Information

• Classes: 4 (Glioma, Meningioma, Pituitary, No Tumor)
• Input Size: 224x224 RGB images
• Architecture: Custom ResidualInceptionBlock with 50+ layers

🛠️ Technology Stack

• PyTorch 2.1.0
• FastAPI 0.104.1
• React 18.2.0
• Vite 5.0.0
• Docker & Docker Compose

📝 License

MIT License

👨‍💻 Author

[Your Name]

🙏 Acknowledgments

• Dataset: [Mention your dataset source]
• Based on ResidualInceptionBlock architecture