Advanced Medical Image Analysis Platform powered by YOLO and DenseNet121 models
- Core Features
- Application UI Demo
- System Overview
- System Architecture
- Development Setup
- API Reference
- Configuration
- Monitoring
- Contributing
- License
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🔍 Advanced Image Analysis
- Chest X-ray abnormality detection
- Skin condition assessment
- Multi-model support
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⚡ Performance
- Real-time image processing
- Optimized YOLO implementations
- Scalable architecture
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🛠 Technical Capabilities
- RESTful API architecture
- Comprehensive metrics monitoring
End-to-end system architecture and data flow
Complete technology stack overview
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Data Processing & ML 🧮
- TensorFlow, PyTorch, scikit-learn, YOLO
- Purpose: Model training and data preprocessing
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AI Server 🤖
- FastAPI, MLflow, DenseNet, YOLO
- Purpose: Model serving and experiment tracking
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Frontend 🎨
- Appsmith
- Purpose: Medical imaging interface
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Backend 📁
- Strapi CMS, SQLite
- Purpose: Patient data management
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Monitoring 📊
- Grafana, Prometheus
- Purpose: System metrics and analytics
Strapi CMS interface for content management
mediscan/
├── .github/ # GitHub-related configurations (CI/CD, issues, PRs)
├── assets/ # Static assets (e.g., images, icons, documentation)
│
├── be-fastapi/ # Core Analysis Engine
│ ├── main.py # Application entrypoint
│ ├── utils/ # Core utilities
│ │ ├── models.py # Model management & YOLO implementations
│ │ ├── image_processing.py # Image preprocessing & augmentation
│ │ └── metrics.py # Performance & inference metrics
│ ├── tests/ # Test suites
│ │ ├── unit/ # Unit tests
│ │ └── integration/ # Integration tests
│ ├── models/ # Pre-trained model storage
│ │ ├── xray/ # X-ray analysis models
│ │ └── skin/ # Skin condition models
│ └── requirements.txt # Python dependencies
│
├── be-fastapi-densenet/ # DenseNet Model Service
│ ├── main.py # DenseNet application entry
│ ├── models/ # DenseNet model files
│ │ └── DenseNet121_epoch_30.keras
│ ├── services/ # Service Layer
│ │ ├── __init__.py
│ │ └── image_service.py
│ ├── utils/ # DenseNet utilities
│ │ ├── __init__.py
│ │ ├── gradcam.py # Grad-CAM visualization
│ ├── .dockerignore
│ ├── .gitignore
│ ├── config.py
│ ├── docker-compose.yml
│ ├── Dockerfile
│ ├── requirements.txt # DenseNet dependencies
│ ├── schemas.py # API schemas
│
├── be-strapi/ # Content Management System
│ ├── api/ # API definitions & routes
│ ├── config/ # CMS configurations
│ ├── scripts/ # Utility scripts
│ │ ├── seed.js # Database seeding
│ │ └── backup.js # Backup utilities
│ ├── data/ # CMS data and content
│ │ ├── uploads/ # Media storage
│ │ └── exports/ # Data exports
│ └── package.json # Node.js dependencies
│
├── dataset/ # Dataset storage and preprocessing
│
├── fe-appsmith/ # Frontend Appsmith integration
│ └── PatientManagementApp.json # Appsmith configurations
│
├── grafana/ # Analytics & Monitoring
│ ├── dashboards/ # Custom dashboard definitions
│ │ ├── system.json # System metrics dashboard
│ │ └── model.json # Model performance dashboard
│ └── provisioning/ # Grafana configurations
│ ├── datasources/ # Data source configs
│ └── notifications/ # Alert configurations
│
├── notebooks/ # Jupyter notebooks for experimentation
│
├── prometheus/ # Monitoring metrics collection
│ └── prometheus.yaml # Prometheus data source configurations
├── .gitattributes
├── CODE_OF_CONDUCT.md # Code of conduct guidelines
├── docker-compose.yml # Docker orchestration
├── LICENSE # Open-source license
└── README.md # Project documentation
- Python 3.8+
- Node.js 18+
- Docker & Docker Compose
- GPU support (recommended)
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Environment Setup
git clone https://github.com/your-org/mediscan.git cd mediscan -
Backend & Monitoring services
# FastAPI Backend cd be-fastapi python -m venv venv source venv/bin/activate # Windows: venv\Scripts\Activate.ps1 pip install -r requirements.txt # Start API Server uvicorn main:app --reload --port 8000 # FastAPI Backend for DenseNet121 cd be-fastapi-densenet python -m venv venv source venv/bin/activate # Windows: venv\Scripts\Activate.ps1 pip install -r requirements.txt # Start API Server uvicorn main:app --reload --port 5000
OR with Docker Compose:
docker-compose up -d
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CMS
# Strapi CMS cd be-strapi npm install npm run develop
| Endpoint | Method | Description |
|---|---|---|
/predict |
POST | Generic prediction pipeline |
/yolo_predict |
POST | X-ray analysis |
/acne-yolo-predict |
POST | Skin condition analysis |
/metrics |
GET | System metrics |
/health |
GET | Service health |
| Variable | Description | Default |
|---|---|---|
CORS_ORIGINS |
Allowed origins | * |
MODEL_PATH |
Model directory | ./models |
PORT |
Service port | 8000 |
- Real-time performance metrics
- Model inference tracking
- System resource monitoring
- Custom Grafana dashboards
- Fork the repository
- Create a feature branch (
git checkout -b feature/enhancement) - Commit changes (
git commit -am 'Add enhancement') - Push branch (
git push origin feature/enhancement) - Open a Pull Request
MIT License - See LICENSE for details