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

2. **Start with Docker Compose**
```bash
docker-compose up --build
```

3. **Access the application**
- Frontend: http://localhost:3000
- API Docs: http://localhost:8000/docs

### Manual Setup

**Backend:**
```bash
cd backend
pip install -r requirements.txt
uvicorn app.main:app --reload
```

**Frontend:**
```bash
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