https://github.com/paulkokos/medscan-platform
AI-Powered Medical Image Segmentation Platform - Full-stack React + Django application with TensorFlow ML integration
https://github.com/paulkokos/medscan-platform
deep-learning django docker full-stack healthcare machine-learning medical-imaging react rest-api tensorflow
Last synced: 4 months ago
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AI-Powered Medical Image Segmentation Platform - Full-stack React + Django application with TensorFlow ML integration
- Host: GitHub
- URL: https://github.com/paulkokos/medscan-platform
- Owner: paulkokos
- License: mit
- Created: 2025-10-11T21:17:03.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-10-16T21:49:10.000Z (9 months ago)
- Last Synced: 2025-10-16T22:14:06.715Z (9 months ago)
- Topics: deep-learning, django, docker, full-stack, healthcare, machine-learning, medical-imaging, react, rest-api, tensorflow
- Language: Python
- Size: 375 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 100
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
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README
# MedScan Platform
> AI-Powered Medical Image Segmentation Platform
[](https://github.com/paulkokos/medscan-platform/actions)
[](https://github.com/paulkokos/medscan-platform/actions)
[](https://github.com/paulkokos/medscan-platform/actions)
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[](http://makeapullrequest.com)
[](https://github.com/psf/black)
[](https://github.com/paulkokos/medscan-platform/discussions)
[](https://github.com/users/paulkokos/projects)
[](https://github.com/paulkokos/medscan-platform/issues)
---
## Overview
MedScan Platform is a full-stack web application designed for medical image analysis and segmentation using deep learning. The platform enables healthcare professionals and researchers to upload medical images (CT scans, MRI, X-rays) and receive automated segmentation results powered by TensorFlow/Keras neural networks.
### Key Features
| Feature | Description |
|---------|-------------|
| **AI-Powered Analysis** | Advanced deep learning models for medical image segmentation |
| **User Authentication** | Secure JWT-based authentication system |
| **Image Management** | Upload, view, and manage medical images |
| **Real-time Processing** | Asynchronous image analysis with progress tracking |
| **Interactive Dashboard** | Visualize segmentation results with Chart.js |
| **RESTful API** | Well-documented API for integration |
| **Responsive Design** | Mobile-friendly interface with Tailwind CSS |
| **Docker Support** | Containerized deployment for easy setup |
| **CI/CD Pipeline** | Automated testing and deployment with GitHub Actions |
---
## Tech Stack
### Frontend
- React 18 with Hooks
- Redux Toolkit for state management
- React Router v6 for client-side routing
- Tailwind CSS for styling
- Axios for HTTP requests
- Chart.js for data visualization
### Backend
- Django 5.0 - High-level Python web framework
- Django REST Framework - API toolkit
- PostgreSQL - Relational database
- TensorFlow/Keras - Deep learning framework
- JWT Authentication - Secure token-based auth
- Pillow - Image processing
### DevOps
- Docker & Docker Compose - Containerization
- GitHub Actions - CI/CD pipelines
- Nginx - Reverse proxy and static file serving
---
## Quick Start
### Prerequisites
```
Node.js 18+
Python 3.11+
PostgreSQL 16+
Docker & Docker Compose (optional)
```
### Installation
#### Option 1: Docker (Recommended)
**1. Clone the repository**
```bash
git clone https://github.com/paulkokos/medscan-platform.git
cd medscan-platform
```
**2. Create environment file**
```bash
cp backend/.env.example backend/.env
# Edit backend/.env with your configuration
```
**3. Start services**
```bash
docker-compose up -d
```
**4. Access the application**
- Frontend: http://localhost:3000
- Backend API: http://localhost:8000
- Admin Panel: http://localhost:8000/admin
#### Option 2: Manual Setup
**Backend Setup:**
```bash
cd backend
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Setup database
createdb medscan
cp .env.example .env
# Edit .env with your database credentials
# Run migrations
python manage.py migrate
# Create superuser
python manage.py createsuperuser
# Run development server
python manage.py runserver
```
**Frontend Setup:**
```bash
cd frontend
# Install dependencies
npm install
# Start development server
npm start
```
**Access the application:**
- Frontend: http://localhost:3000
- Backend: http://localhost:8000
---
## Project Structure
```
medscan-platform/
├── .github/
│ ├── workflows/ # CI/CD workflows
│ └── ISSUE_TEMPLATE/ # Issue templates
├── frontend/ # React application
│ ├── public/
│ └── src/
│ ├── components/ # React components
│ ├── pages/ # Page components
│ ├── redux/ # State management
│ └── services/ # API services
├── backend/ # Django application
│ ├── apps/
│ │ ├── authentication/ # User authentication
│ │ ├── images/ # Image management
│ │ └── analysis/ # ML analysis
│ └── medscan/ # Project settings
└── docker-compose.yml # Docker orchestration
```
---
## API Documentation
### Authentication Endpoints
| Method | Endpoint | Description |
|--------|----------|-------------|
| POST | `/api/auth/register/` | Register new user |
| POST | `/api/auth/login/` | Login and get JWT token |
| GET | `/api/auth/user/` | Get current user profile |
### Images Endpoints
| Method | Endpoint | Description |
|--------|----------|-------------|
| GET | `/api/images/` | List all images |
| POST | `/api/images/` | Upload new image |
| GET | `/api/images/{id}/` | Get image details |
| DELETE | `/api/images/{id}/` | Delete image |
### Analysis Endpoints
| Method | Endpoint | Description |
|--------|----------|-------------|
| POST | `/api/analysis/` | Start analysis |
| GET | `/api/analysis/{id}/` | Get analysis results |
For detailed API documentation, visit `/api/docs/` when running the server.
---
## Development
### Running Tests
**Frontend:**
```bash
cd frontend
npm test # Run tests
npm run test:coverage # Run with coverage
npm run lint # Run ESLint
npm run format # Format with Prettier
```
**Backend:**
```bash
cd backend
pytest # Run tests
pytest --cov # Run with coverage
black . # Format code
pylint **/*.py # Lint code
```
### Code Style
- **Frontend**: ESLint + Prettier (Airbnb style guide)
- **Backend**: Black + isort + Pylint (PEP 8)
### Git Workflow
We follow the [GitHub Flow](https://guides.github.com/introduction/flow/):
1. Create a feature branch from `main`
2. Make changes and commit
3. Push and create a pull request
4. Code review and CI checks
5. Merge to `main`
See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines.
---
## Deployment
### Production Deployment
1. Update environment variables for production
2. Build Docker images
3. Deploy using docker-compose or orchestration tool
```bash
docker-compose -f docker-compose.prod.yml up -d
```
### Environment Variables
See `backend/.env.example` for required environment variables.
---
## Security
For security vulnerabilities, please see [SECURITY.md](SECURITY.md).
**Security Features:**
- Secret Scanning with Push Protection
- Dependabot Security Updates
- Branch Protection Rules
- CodeQL Analysis (JavaScript + Python)
- SAST Scanning with Semgrep
---
## Contributing
### [Join the Discussion](https://github.com/paulkokos/medscan-platform/discussions)
- Ask questions and get help from the community
- Share ideas for new features and improvements
- Showcase your implementations and use cases
- Discuss medical imaging research and techniques
- Collaborate on development and best practices
### [Project Board](https://github.com/users/paulkokos/projects)
- View current development priorities and roadmap
- See what features are planned and in progress
- Contribute to ongoing tasks and initiatives
- Track bug fixes and feature requests
### How to Contribute
We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for details.
**Quick Steps:**
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
---
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---
## Authors
**Paul Kokos** - [paulkokos](https://github.com/paulkokos)
---
## Acknowledgments
- TensorFlow team for the deep learning framework
- Django REST Framework for the excellent API toolkit
- React team for the amazing UI library
- All contributors who help improve this project
---
## Project Status
**Current Version:** 0.1.0 (Alpha)
This project is under active development. Features and API may change.
### Roadmap
- [ ] Implement U-Net architecture for segmentation
- [ ] Add support for DICOM file format
- [ ] Real-time collaboration features
- [ ] Export results to PDF reports
- [ ] Integration with PACS systems
- [ ] Multi-language support
- [ ] Mobile application
---
## Contact
**Project Link:** [https://github.com/paulkokos/medscan-platform](https://github.com/paulkokos/medscan-platform)
---
Made with care for the medical imaging community