Skip to content

README.md

Latest commit

 

History

History
146 lines (103 loc) · 2.88 KB

README.md

File metadata and controls

146 lines (103 loc) · 2.88 KB
Raw

🧠 Neuro Tech Care (Medalgnosis)

🚀 AI-Powered Brain Tumor Diagnosis with Privacy-First Approach

📌 Overview

Neuro Tech Care is an intelligent medical application designed to revolutionize brain tumor diagnosis using AI-driven MRI analysis.

The system leverages Microsoft Azure Custom Vision to detect brain tumors such as:

  • Meningioma
  • Glioma
  • Pituitary Tumors

With a strong focus on data privacy, accuracy, and accessibility, the platform ensures that no user data or MRI scans are stored, making it a secure and patient-centric solution.

🎯 Problem Statement

Traditional brain tumor diagnosis using MRI scans:

  • Time-consuming
  • Prone to human error
  • Lacks strong privacy mechanisms

💡 Solution

Medalgnosis introduces:

  • Fast AI-based MRI analysis
  • Accurate tumor detection using Azure AI
  • Privacy-first architecture (no data storage)
  • Detailed tumor insights & treatment guidance
  • Seamless doctor appointment integration

🌍 Theme

Creating a brighter future for health by leveraging cutting-edge technology and compassionate care.

✨ Key Features

  • AI Tumor Detection
    Detects multiple tumor types using trained ML models

  • Detailed Medical Insights
    Provides:

    • Tumor characteristics
    • Causes & symptoms
    • Effects & risks
    • Treatment options

  • Personalized Recommendations
    Suggests treatment paths based on tumor type

  • Privacy First

    • No data storage
    • No MRI retention
    • Secure processing

  • Doctor Appointment Integration
    Powered by Azure Logic Apps

  • User-Friendly Interface
    Built using Streamlit for easy interaction

🛠️ Tech Stack

  • Python
  • Streamlit
  • Streamlit Community Cloud
  • Microsoft Azure Custom Vision
  • Azure Logic Apps
  • GitHub

🧑‍💻 Developer

Aaqeeb Pinjari

  • Location: Aurangabad, India
  • Graduation Year: 2024

💼 Role & Contribution

  • Worked on Azure backend integration
  • Implemented Azure Custom Vision model pipeline
  • Integrated AI model with Streamlit frontend
  • Connected Azure Logic Apps for appointment workflow

⚙️ Installation Guide

🔹 Prerequisites

  • Python (3.x)
  • Azure Account

👉 Get Azure: https://azure.microsoft.com/en-in/free

🔹 Setup Steps

  1. Clone the repository:
git clone <your-repo-link>
cd <project-folder>
  1. Install dependencies:
pip install -r requirements.txt

  1. Configure Azure: Create Azure Custom Vision resource Train model Get Endpoint & Key Configure Logic App: Create Logic App with HTTP Trigger Add Send Email (V2) action

  2. Edit predictor.py: Replace Azure Endpoint Replace API Key Replace Logic App URL

  3. ▶️ Run Application

streamlit run about.py

⭐ Support If you like this project:

Star the repo Fork it Contribute