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Medical-report-analyzer

Analyze medical reports and symptoms to gain health insights and suggestions, providing detailed medicine information tailored to individual needs with bilingual support in English and Bengali.

Author

Medical-report-analyzer logo

TanvirHafiz

MIT License

Quick Info

GitHub GitHub Stars 0
NPM Weekly Downloads 0
Tools 1
Last Updated 2026-02-19

Tags

tanvirhafizbengalibilingualtanvirhafiz medicaltranslation tanvirhafizmedical reports

Medical Report Analyzer

A web application that provides medical report analysis, symptoms analysis, and medicine information using AI. The application supports both English and Bengali (বাংলা) languages.

Features

  1. Medical Report Analysis
  2. Upload medical reports (JPG, PDF)
  3. Extract and analyze test results
  4. Get health insights and suggestions

  5. Symptoms Analysis

  6. Describe symptoms in detail
  7. Get potential conditions and urgency level
  8. Receive immediate steps and precautions

  9. Medicine Information

  10. Get detailed medicine analysis
  11. View usage, side effects, and precautions
  12. Personalized information based on age and gender
  13. Dosage schedule analysis

  14. Bilingual Support

  15. Toggle between English and Bengali
  16. Instant translation of analysis results

Technologies Used

  • Python/Flask (Backend)
  • JavaScript/HTML/CSS (Frontend)
  • Tailwind CSS (Styling)
  • Ollama with deepseek-r1:14b model (AI Analysis)
  • Tesseract OCR (Text Extraction)
  • Google Translate API (Translation)

Prerequisites

  1. Python 3.8 or higher
  2. Tesseract OCR installed
  3. Ollama with deepseek-r1:14b model

Installation

  1. Clone the repository:
git clone <repository-url>
cd medical-report-analyzer
  1. Create a virtual environment:
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt
  1. Install Tesseract OCR:
  2. Windows: Download and install from Tesseract GitHub
  3. Linux: sudo apt-get install tesseract-ocr
  4. Mac: brew install tesseract

  5. Install and run Ollama:

  6. Follow instructions at Ollama
  7. Pull the model: ollama pull deepseek-r1:14b

Configuration

  1. Set Tesseract path in app.py:
pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'  # Adjust path as needed
  1. Ensure Ollama is running with the deepseek-r1:14b model:
ollama run deepseek-r1:14b

Running the Application

  1. Start the Flask server:
python app.py
  1. Open a web browser and navigate to:
http://localhost:5000

Usage

  1. Analyzing Medical Reports
  2. Click "Report Analysis" tab
  3. Upload JPG or PDF file
  4. View analysis results
  5. Optionally translate to Bengali

  6. Analyzing Symptoms

  7. Click "Symptoms Analysis" tab
  8. Describe symptoms in detail
  9. Click "Analyze Symptoms"
  10. View analysis and recommendations

  11. Getting Medicine Information

  12. Click "Medicine Info" tab
  13. Enter patient age and gender
  14. Input medicine name and dosage schedule
  15. Click "Analyze Medicine"
  16. View detailed medicine analysis

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

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

See Also

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