Medical Report Analyzer

Medical Report Analyzer

Contribute to TanvirHafiz/Medical-report-analyzer development by creating an account on GitHub.

TanvirHafiz

Digital Note Management
Content Fetching
Database Interaction
AI Content Generation
Data & App Analysis
Visit Server

README

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 <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:

    • 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.

Recommended Servers

VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Mult Fetch MCP Server

Mult Fetch MCP Server

A versatile MCP-compliant web content fetching tool that supports multiple modes (browser/node), formats (HTML/JSON/Markdown/Text), and intelligent proxy detection, with bilingual interface (English/Chinese).

Featured
Local
AIO-MCP Server

AIO-MCP Server

🚀 All-in-one MCP server with AI search, RAG, and multi-service integrations (GitLab/Jira/Confluence/YouTube) for AI-enhanced development workflows. Folk from

Featured
Local
Persistent Knowledge Graph

Persistent Knowledge Graph

An implementation of persistent memory for Claude using a local knowledge graph, allowing the AI to remember information about users across conversations with customizable storage location.

Featured
Local
Hyperbrowser MCP Server

Hyperbrowser MCP Server

Welcome to Hyperbrowser, the Internet for AI. Hyperbrowser is the next-generation platform empowering AI agents and enabling effortless, scalable browser automation. Built specifically for AI developers, it eliminates the headaches of local infrastructure and performance bottlenecks, allowing you to

Featured
Local
React MCP

React MCP

react-mcp integrates with Claude Desktop, enabling the creation and modification of React apps based on user prompts

Featured
Local
Any OpenAI Compatible API Integrations

Any OpenAI Compatible API Integrations

Integrate Claude with Any OpenAI SDK Compatible Chat Completion API - OpenAI, Perplexity, Groq, xAI, PyroPrompts and more.

Featured
Exa MCP

Exa MCP

A Model Context Protocol server that enables AI assistants like Claude to perform real-time web searches using the Exa AI Search API in a safe and controlled manner.

Featured