Built by Metorial, the integration platform for agentic AI.

Learn More

TanvirHafiz/Medical-report-analyzer

Medical Report Analyzer

    Server Summary

    • Upload medical reports

    • Extract and analyze test results

    • Describe symptoms and assess conditions

    • Provide health suggestions and precautions

    • Detail medicine usage and side effects

    • Personalized dosage schedule analysis

    • Toggle between English and Bengali

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

    • Upload medical reports (JPG, PDF)
    • Extract and analyze test results
    • Get health insights and suggestions
  2. Symptoms Analysis

    • Describe symptoms in detail
    • Get potential conditions and urgency level
    • Receive immediate steps and precautions
  3. Medicine Information

    • Get detailed medicine analysis
    • View usage, side effects, and precautions
    • Personalized information based on age and gender
    • Dosage schedule analysis
  4. Bilingual Support

    • Toggle between English and Bengali
    • 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 
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:

    • Windows: Download and install from Tesseract GitHub
    • Linux: sudo apt-get install tesseract-ocr
    • Mac: brew install tesseract
  2. Install and run Ollama:

    • Follow instructions at Ollama
    • 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

    • Click "Report Analysis" tab
    • Upload JPG or PDF file
    • View analysis results
    • Optionally translate to Bengali
  2. Analyzing Symptoms

    • Click "Symptoms Analysis" tab
    • Describe symptoms in detail
    • Click "Analyze Symptoms"
    • View analysis and recommendations
  3. Getting Medicine Information

    • Click "Medicine Info" tab
    • Enter patient age and gender
    • Input medicine name and dosage schedule
    • Click "Analyze Medicine"
    • 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.