https://github.com/anhtuan284/mediscan
A R&D project about apply LCDP and Computer Vision in building medical disease segmentation system
https://github.com/anhtuan284/mediscan
appsmith computer-vision densenet121 lcdp strapi-cms tensorflow yolo
Last synced: about 1 month ago
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A R&D project about apply LCDP and Computer Vision in building medical disease segmentation system
- Host: GitHub
- URL: https://github.com/anhtuan284/mediscan
- Owner: anhtuan284
- License: mit
- Created: 2025-02-28T15:40:03.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-17T03:57:37.000Z (about 1 year ago)
- Last Synced: 2025-06-03T17:30:16.211Z (about 1 year ago)
- Topics: appsmith, computer-vision, densenet121, lcdp, strapi-cms, tensorflow, yolo
- Language: Jupyter Notebook
- Homepage:
- Size: 104 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# MediScan
> Advanced Medical Image Analysis Platform powered by YOLO and DenseNet121 models
## Table of Contents
- [Core Features](#core-features)
- [Application UI Demo](#application-ui-demo)
- [System Dashboard](#system-dashboard)
- [Medical Analysis Interface](#medical-analysis-interface)
- [System Overview](#system-overview)
- [System Flow Architecture](#system-flow-architecture)
- [Technology Stack](#technology-stack)
- [Content Management System](#content-management-system)
- [System Architecture](#system-architecture)
- [Development Setup](#development-setup)
- [API Reference](#api-reference)
- [Configuration](#configuration)
- [Monitoring](#monitoring)
- [Contributing](#contributing)
- [License](#license)
## Core Features
- 🔍 **Advanced Image Analysis**
- Chest X-ray abnormality detection
- Skin condition assessment
- Multi-model support
- ⚡ **Performance**
- Real-time image processing
- Optimized YOLO implementations
- Scalable architecture
- 🛠 **Technical Capabilities**
- RESTful API architecture
- Comprehensive metrics monitoring
## Application UI Demo
### Medical Analysis Interface
### System Dashboard
## System Overview
### System Flow Architecture
*End-to-end system architecture and data flow*
### Technology Stack
*Complete technology stack overview*
#### Tech Stack Breakdown
- **Data Processing & ML** 🧮
- TensorFlow, PyTorch, scikit-learn, YOLO
- Purpose: Model training and data preprocessing
- **AI Server** 🤖
- FastAPI, MLflow, DenseNet, YOLO
- Purpose: Model serving and experiment tracking
- **Frontend** 🎨
- Appsmith
- Purpose: Medical imaging interface
- **Backend** 📁
- Strapi CMS, SQLite
- Purpose: Patient data management
- **Monitoring** 📊
- Grafana, Prometheus
- Purpose: System metrics and analytics
---
### Content Management System
*Strapi CMS interface for content management*
## System Architecture
```ascii
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
```
## Development Setup
### Requirements
- Python 3.8+
- Node.js 18+
- Docker & Docker Compose
- GPU support (recommended)
### Quick Start
1. **Environment Setup**
```bash
git clone https://github.com/your-org/mediscan.git
cd mediscan
```
2. **Backend & Monitoring services**
```bash
# 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:
```bash
docker-compose up -d
```
3. **CMS**
```bash
# Strapi CMS
cd be-strapi
npm install
npm run develop
```
## API Reference
### Core Endpoints
| 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 |
## Configuration
### Environment Variables
| Variable | Description | Default |
|----------|-------------|---------|
| `CORS_ORIGINS` | Allowed origins | `*` |
| `MODEL_PATH` | Model directory | `./models` |
| `PORT` | Service port | `8000` |
## Monitoring
- Real-time performance metrics
- Model inference tracking
- System resource monitoring
- Custom Grafana dashboards
## Contributing
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/enhancement`)
3. Commit changes (`git commit -am 'Add enhancement'`)
4. Push branch (`git push origin feature/enhancement`)
5. Open a Pull Request
## License
MIT License - See [LICENSE](LICENSE) for details