{"id":26451078,"url":"https://github.com/anhtuan284/mediscan","last_synced_at":"2026-04-29T15:32:28.462Z","repository":{"id":279991093,"uuid":"940675454","full_name":"anhtuan284/mediscan","owner":"anhtuan284","description":"A R\u0026D project about apply LCDP and Computer Vision in building medical disease segmentation system","archived":false,"fork":false,"pushed_at":"2025-03-17T03:57:37.000Z","size":108543,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-03T17:30:16.211Z","etag":null,"topics":["appsmith","computer-vision","densenet121","lcdp","strapi-cms","tensorflow","yolo"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["appsmith","computer-vision","densenet121","lcdp","strapi-cms","tensorflow","yolo"],"created_at":"2025-03-18T16:31:36.201Z","updated_at":"2026-04-29T15:32:28.445Z","avatar_url":"https://github.com/anhtuan284.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MediScan\n\n\u003e Advanced Medical Image Analysis Platform powered by YOLO and DenseNet121 models\n\u003cdiv \u003e\n  \u003ca href=\"https://www.tensorflow.org/\"\u003e\u003cimg src=\"https://img.shields.io/badge/TensorFlow-FF3F06?style=for-the-badge\u0026logo=tensorflow\u0026logoColor=white\" alt=\"tensorflow\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://fastapi.tiangolo.com/\"\u003e\u003cimg src=\"https://img.shields.io/badge/FastAPI-005571?style=for-the-badge\u0026logo=fastapi\" alt=\"fastapi\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://www.appsmith.com/\"\u003e\u003cimg src=\"https://img.shields.io/badge/Appsmith-000000?style=for-the-badge\u0026logo=appsmith\u0026logoColor=yellow\" alt=\"appsmith\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://strapi.io/\"\u003e\u003cimg src=\"https://img.shields.io/badge/strapi-%232E7EEA.svg?style=for-the-badge\u0026logo=strapi\u0026logoColor=white\" alt=\"strapi\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://www.docker.com/\"\u003e\u003cimg src=\"https://img.shields.io/badge/docker-%230db7ed.svg?style=for-the-badge\u0026logo=docker\u0026logoColor=white\" alt=\"docker\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://grafana.com/\"\u003e\u003cimg src=\"https://img.shields.io/badge/grafana-%23F46800.svg?style=for-the-badge\u0026logo=grafana\u0026logoColor=white\" alt=\"grafana\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://ultralytics.com/yolov8\"\u003e\u003cimg src=\"https://img.shields.io/badge/YOLO-00FFFF?style=for-the-badge\u0026logo=yolo\u0026logoColor=black\" alt=\"yolo\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n## Table of Contents\n- [Core Features](#core-features)\n- [Application UI Demo](#application-ui-demo)\n  - [System Dashboard](#system-dashboard)\n  - [Medical Analysis Interface](#medical-analysis-interface)\n- [System Overview](#system-overview)\n  - [System Flow Architecture](#system-flow-architecture)\n  - [Technology Stack](#technology-stack)\n  - [Content Management System](#content-management-system)\n- [System Architecture](#system-architecture)\n- [Development Setup](#development-setup)\n- [API Reference](#api-reference)\n- [Configuration](#configuration)\n- [Monitoring](#monitoring)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Core Features\n\n- 🔍 **Advanced Image Analysis**\n  - Chest X-ray abnormality detection\n  - Skin condition assessment\n  - Multi-model support\n  \n- ⚡ **Performance**\n  - Real-time image processing\n  - Optimized YOLO implementations\n  - Scalable architecture\n\n- 🛠 **Technical Capabilities**\n  - RESTful API architecture\n  - Comprehensive metrics monitoring\n\n## Application UI Demo\n\n\n\n### Medical Analysis Interface\n\u003cdiv style=\"display: grid; grid-template-columns: repeat(2, 1fr); gap: 20px; margin: 20px 0;\"\u003e\n    \u003cimg src=\"./assets/images/demo1.png\" alt=\"DenseNet121 Prediction\" width=\"400\"/\u003e\n    \u003cimg src=\"./assets/images/demo2.png\" alt=\"Disease Probability\" width=\"400\"/\u003e\n    \u003cimg src=\"./assets/images/demo3.png\" alt=\"YOLO Detection\" width=\"400\"/\u003e\n    \u003cimg src=\"./assets/images/demo4.png\" alt=\"Acne Detection\" width=\"400\"/\u003e\n\u003c/div\u003e\n\n### System Dashboard\n\u003cdiv style=\"margin: 20px 0;\"\u003e\n    \u003cimg src=\"./assets/images/monitor.png\" alt=\"System Monitoring Dashboard\" width=\"100%\"/\u003e\n\u003c/div\u003e\n\n## System Overview\n\n### System Flow Architecture\n![System Flow](./assets/images/systemflow.png)\n*End-to-end system architecture and data flow*\n\n### Technology Stack\n![Tech Stack](./assets/images/techstack.png)\n*Complete technology stack overview*\n\n#### Tech Stack Breakdown\n\n- **Data Processing \u0026 ML** 🧮\n  - TensorFlow, PyTorch, scikit-learn, YOLO\n  - Purpose: Model training and data preprocessing\n\n- **AI Server** 🤖\n  - FastAPI, MLflow, DenseNet, YOLO\n  - Purpose: Model serving and experiment tracking\n\n- **Frontend** 🎨\n  - Appsmith\n  - Purpose: Medical imaging interface\n\n- **Backend** 📁\n  - Strapi CMS, SQLite\n  - Purpose: Patient data management\n\n- **Monitoring** 📊\n  - Grafana, Prometheus\n  - Purpose: System metrics and analytics\n\n---\n\n### Content Management System\n![Strapi CMS](./assets/images/strapi.png)\n*Strapi CMS interface for content management*\n\n## System Architecture\n\n```ascii\nmediscan/\n├── .github/                    # GitHub-related configurations (CI/CD, issues, PRs)\n├── assets/                     # Static assets (e.g., images, icons, documentation)\n│\n├── be-fastapi/                 # Core Analysis Engine\n│   ├── main.py                 # Application entrypoint\n│   ├── utils/                   # Core utilities\n│   │   ├── models.py            # Model management \u0026 YOLO implementations\n│   │   ├── image_processing.py  # Image preprocessing \u0026 augmentation\n│   │   └── metrics.py           # Performance \u0026 inference metrics\n│   ├── tests/                   # Test suites\n│   │   ├── unit/                # Unit tests\n│   │   └── integration/         # Integration tests\n│   ├── models/                  # Pre-trained model storage\n│   │   ├── xray/                # X-ray analysis models\n│   │   └── skin/                # Skin condition models\n│   └── requirements.txt         # Python dependencies\n│\n├── be-fastapi-densenet/        # DenseNet Model Service\n│   ├── main.py                 # DenseNet application entry\n│   ├── models/                 # DenseNet model files\n│   │   └── DenseNet121_epoch_30.keras\n│   ├── services/               # Service Layer\n│   │   ├── __init__.py\n│   │   └── image_service.py\n│   ├── utils/                  # DenseNet utilities\n│   │   ├── __init__.py\n│   │   ├── gradcam.py          # Grad-CAM visualization\n│   ├── .dockerignore\n│   ├── .gitignore\n│   ├── config.py\n│   ├── docker-compose.yml\n│   ├── Dockerfile\n│   ├── requirements.txt        # DenseNet dependencies\n│   ├── schemas.py              # API schemas\n│\n├── be-strapi/                  # Content Management System\n│   ├── api/                    # API definitions \u0026 routes\n│   ├── config/                 # CMS configurations\n│   ├── scripts/                # Utility scripts\n│   │   ├── seed.js             # Database seeding\n│   │   └── backup.js           # Backup utilities\n│   ├── data/                   # CMS data and content\n│   │   ├── uploads/            # Media storage\n│   │   └── exports/            # Data exports\n│   └── package.json            # Node.js dependencies\n│\n├── dataset/                    # Dataset storage and preprocessing\n│\n├── fe-appsmith/                # Frontend Appsmith integration\n│   └── PatientManagementApp.json  # Appsmith configurations\n│\n├── grafana/                    # Analytics \u0026 Monitoring\n│   ├── dashboards/             # Custom dashboard definitions\n│   │   ├── system.json         # System metrics dashboard\n│   │   └── model.json          # Model performance dashboard\n│   └── provisioning/           # Grafana configurations\n│       ├── datasources/        # Data source configs\n│       └── notifications/      # Alert configurations\n│\n├── notebooks/                  # Jupyter notebooks for experimentation\n│\n├── prometheus/                 # Monitoring metrics collection\n│   └── prometheus.yaml         # Prometheus data source configurations\n├── .gitattributes\n├── CODE_OF_CONDUCT.md          # Code of conduct guidelines\n├── docker-compose.yml          # Docker orchestration\n├── LICENSE                     # Open-source license\n└── README.md                   # Project documentation\n\n```\n\n## Development Setup\n\n### Requirements\n\n- Python 3.8+\n- Node.js 18+\n- Docker \u0026 Docker Compose\n- GPU support (recommended)\n\n### Quick Start\n\n1. **Environment Setup**\n   ```bash\n   git clone https://github.com/your-org/mediscan.git\n   cd mediscan\n   ```\n\n2. **Backend \u0026 Monitoring services**\n   ```bash\n   # FastAPI Backend\n   cd be-fastapi\n   python -m venv venv\n   source venv/bin/activate  # Windows: venv\\Scripts\\Activate.ps1\n   pip install -r requirements.txt\n   \n   # Start API Server\n   uvicorn main:app --reload --port 8000\n\n   # FastAPI Backend for DenseNet121\n   cd be-fastapi-densenet\n   python -m venv venv\n   source venv/bin/activate  # Windows: venv\\Scripts\\Activate.ps1\n   pip install -r requirements.txt\n   \n   # Start API Server\n   uvicorn main:app --reload --port 5000\n   ```\n   OR with Docker Compose:\n   ```bash\n   docker-compose up -d\n   ```\n\n3. **CMS**\n   ```bash\n   # Strapi CMS\n   cd be-strapi\n   npm install\n   npm run develop\n   ```\n\n## API Reference\n\n### Core Endpoints\n\n| Endpoint | Method | Description |\n|----------|--------|-------------|\n| `/predict` | POST | Generic prediction pipeline |\n| `/yolo_predict` | POST | X-ray analysis |\n| `/acne-yolo-predict` | POST | Skin condition analysis |\n| `/metrics` | GET | System metrics |\n| `/health` | GET | Service health |\n\n## Configuration\n\n### Environment Variables\n\n| Variable | Description | Default |\n|----------|-------------|---------|\n| `CORS_ORIGINS` | Allowed origins | `*` |\n| `MODEL_PATH` | Model directory | `./models` |\n| `PORT` | Service port | `8000` |\n\n## Monitoring\n\n- Real-time performance metrics\n- Model inference tracking\n- System resource monitoring\n- Custom Grafana dashboards\n\n## Contributing\n\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/enhancement`)\n3. Commit changes (`git commit -am 'Add enhancement'`)\n4. Push branch (`git push origin feature/enhancement`)\n5. Open a Pull Request\n\n\n## License\n\nMIT License - See [LICENSE](LICENSE) for details","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanhtuan284%2Fmediscan","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanhtuan284%2Fmediscan","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanhtuan284%2Fmediscan/lists"}