MedScan - Medication Management System

Note: This project is a Capstone Project for Seneca College. Disclaimer: This software is provided for educational purposes only as part of a student capstone project. It is not intended for medical, diagnostic, or commercial use. Use at your own risk.

MedScan is a local-first medication management application designed to help elderly users manage their medications safely and effectively. The system uses computer vision to identify pills and OCR to read prescription information.

• 🎯 Pill detection using YOLOv8
• 📝 Prescription text extraction using Tesseract OCR
• ⏰ Medication reminders and scheduling
• 👴 Senior-friendly user interface
• 💾 Local-first operation (no cloud dependencies)
• 📱 Offline functionality
• 📊 Excel-based history tracking with JSON backup
• 📝 Comprehensive logging system
• 🔄 Synchronized data storage

medscan/
├── frontend/                    # Frontend application
│   ├── index.html              # Main page
│   ├── scan.html               # Pill scanning page
│   ├── reminder.html           # Reminder settings page
│   ├── history.html            # Medication history page
│   └── static/                 # Static assets
│       ├── style.css           # Main stylesheet
│       └── script.js           # Frontend JavaScript
│
├── backend_flask/              # Flask backend application
│   ├── app.py                 # Main Flask application
│   ├── requirements.txt       # Python dependencies
│   └── utils/                 # Utility modules
│       ├── predict.py         # Pill detection logic
│       ├── ocr.py            # Prescription text extraction
│       ├── history_manager.py # History tracking with sync
│       └── train_yolo.py     # YOLOv8 training script
│
├── models/                    # ML models
│   └── yolov8/               # YOLOv8 models
│
├── memory/                   # Local storage
│   ├── medication_history.xlsx  # Excel history file
│   └── medication_history.json  # JSON backup file
│
└── README.md                # This file

• Python 3.8 or higher
• Tesseract OCR installed on your system
• Webcam or camera for pill scanning
• Modern web browser

1. Clone the repository:

git clone https://github.com/Masoud-Masoori/MedScan.git
cd MedScan

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

cd backend_flask
pip install -r requirements.txt

4. Install Tesseract OCR:

• Windows: Download and install from https://github.com/UB-Mannheim/tesseract/wiki
• Linux: sudo apt-get install tesseract-ocr
• macOS: brew install tesseract

1. Start the Flask server:

cd backend_flask
python app.py

2. Open your web browser and navigate to:

http://localhost:5000

3. Use the application:

• Click "Scan Pill" to identify a medication
• Set reminders for medication times
• View medication history
• Export history to Excel

• Uses YOLOv8 for accurate pill identification
• Works with various pill shapes and colors
• Provides confidence scores
• Real-time detection with webcam support

• Extracts text from prescription images
• Identifies medication names, dosages, and instructions
• Supports multiple prescription formats

• Set custom medication schedules
• Daily, weekly, or custom frequency
• Visual and audio notifications

• Excel-based storage for medication history
• JSON backup for data redundancy
• Automatic synchronization between storage formats
• Status tracking (taken/pending)
• Export functionality
• Comprehensive logging system for debugging

• Automatic synchronization between Excel and JSON formats
• Backup system to prevent data loss
• Logging system for tracking operations
• Error handling and recovery

The application includes a comprehensive logging system that tracks:

• Application startup and shutdown
• Pill detection operations
• History management operations
• File synchronization events
• Error conditions and recovery attempts

Logs can be found in the application's console output and can be redirected to a file if needed.

The application includes robust error handling for:

• File operations
• Data synchronization
• Pill detection
• OCR processing
• History management

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

This project is licensed under the MIT License - see the LICENSE file for details.

• YOLOv8 by Ultralytics
• Tesseract OCR
• Flask framework
• Python logging system