🫀 Automated cardiac MRI segmentation — U-Net precisely delineates Left Ventricle, Right Ventricle & Myocardium with 94.8% accuracy, empowering faster and more reliable clinical diagnostics.

⚕️ Medical Disclaimer: This system is an AI-assisted screening tool designed to support qualified medical professionals. All predictions require review by a licensed cardiologist before any clinical decision is made.

🚀 Quick Start  •  🏗️ Architecture  •  📸 Screenshots  •  📊 Results  •  👥 Team  •  ☕ Support

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🏆 Why HeartSeg AI?

Traditional Segmentation   →   Manual, hours per scan, error-prone, inconsistent across radiologists
HeartSeg AI                →   Automated, sub-minute inference, 94.8% accuracy, reproducible results

94.8% Accuracy Precise pixel-wise segmentation of all 3 cardiac structures

U-Net Architecture State-of-the-art encoder-decoder with skip connections

Real-Time Results Instant segmentation feedback via web interface

6 Disease Classes Normal + 5 cardiac pathology classifications

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🌟 Project Overview

HeartSeg AI is a deep learning-powered cardiac MRI segmentation system built as a Mini Project at Saveetha Engineering College. It implements the U-Net architecture to automatically segment critical heart structures — Left Ventricle, Right Ventricle, and Myocardium — from MRI scans, while also classifying the scan into one of 6 cardiac disease categories through a clean web interface.

🎓 Institution: Saveetha Engineering College, Chennai 📅 Academic Year: 2024–2025 🧠 Model: U-Net with 94.8% segmentation accuracy 🏥 Clinical Use: Cardiac MRI diagnostic support

🎯 Problem Statement

Manual cardiac MRI segmentation is a bottleneck in clinical cardiology — it takes hours per scan, requires expert radiologists, and produces inconsistent results across practitioners. HeartSeg AI automates this entirely, delivering reproducible, high-accuracy segmentation in under a minute through a browser-based interface that integrates seamlessly into clinical workflows.

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✨ Feature Highlights

🧠 U-Net Segmentation Engine

• Encoder Path — Captures multi-scale contextual features through progressive downsampling
• Bottleneck — Processes the most compressed, abstract feature representation
• Decoder Path — Precise localization through upsampling with skip connections
• Pixel-wise Output — Generates full-resolution segmentation masks
• 3 Structure Segmentation — Left Ventricle, Right Ventricle, Myocardium simultaneously
• 94.8% accuracy on validation MRI datasets

🏥 6-Class Disease Classification

• Normal — Healthy cardiac MRI
• Coronary Artery Disease — Arterial blockage patterns
• Chronic Ischemic Disease — Chronic blood flow restriction
• Heart Failure — Reduced ejection fraction indicators
• Heart Valve Disease — Structural valve abnormalities
• Irregular Heartbeat — Arrhythmia-related structural changes

🌐 Web-Based Clinical Interface

• Secure login system with session management
• Drag-and-drop MRI image upload
• Real-time segmentation visualization
• Overlay of predicted mask on original MRI
• Clean, responsive dark-themed UI
• Built with Flask + HTML5/CSS3

⚡ Automated Processing Pipeline

• Image normalization and preprocessing on upload
• Automatic model inference via mri_segmentation.py
• Post-processing and mask overlay generation
• Instant result rendering in browser
• No manual steps between upload and result

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🏗️ System Architecture

U-Net encoder-decoder architecture: MRI input → feature extraction → pixel-wise segmentation mask

Note: See the architecture-diagram.html file for an interactive version of this diagram.

🧩 Component Summary

Component	File	Technology	Purpose
Web Server	app.py	Flask	Routes, session auth, file handling
Segmentation Engine	mri_segmentation.py	TensorFlow / Keras	U-Net inference pipeline
Trained Model	h5/heart_mri_model.h5	Keras SavedModel	Pre-trained U-Net weights
Training Pipeline	train.py	TensorFlow 2.4.1	Model training and evaluation
Login UI	templates/login.html	HTML5 + CSS3	Authentication interface
Upload UI	templates/upload.html	HTML5 + CSS3	MRI image submission
Result UI	templates/result.html	HTML5 + CSS3	Segmentation visualization
Styling	static/*.css	CSS3	Page-specific stylesheets

🔄 Inference Flow

User Login (Flask Session)
         │
         ▼
MRI Image Upload (JPG/PNG)
         │
         ▼
Preprocessing — Normalize · Resize to (256×256) · Expand dims
         │
         ▼
U-Net Model Inference (heart_mri_model.h5)
    Encoder → Bottleneck → Decoder
         │
         ▼
Segmentation Mask (pixel-wise prediction)
         │
         ▼
Disease Classification (6 classes)
         │
         ▼
Overlay Visualization + Result Display

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📸 Screenshots

🔑 Authentication

Login Page

📤 Upload Interface

Upload Page	Image Selection

🔬 Segmentation Results

Normal	Coronary Artery Disease

Chronic Ischemic Disease	Heart Failure

Heart Valve Disease	Irregular Heartbeat

🖥️ Development Environment

VS Code — Running Server

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📂 Project Structure

Miniproject/
│
├── 📁 h5/
│   └── heart_mri_model.h5             # Pre-trained U-Net weights
│
├── 📁 img/                             # Screenshots & diagrams (10 images)
│   ├── Login_Page.png
│   ├── Upload_Page.png
│   ├── Upload_Image_Selection_Page.png
│   ├── Prediction_Result_Normal.png
│   ├── Prediction_Result_Choronary_Artery_Disease.png
│   ├── Prediction_Result_Chronic_Ischemic_Disease.png
│   ├── Prediction_Result_Heart_Failure_Disease.png
│   ├── Prediction_Result_Heart_Valve_Disease.png
│   ├── Prediction_Result_Irregular_Heartbeat_Disease.png
│   └── VS_Code_Running_Status.png
│
├── 📁 static/                          # CSS stylesheets
│   ├── login.css
│   ├── upload.css
│   └── result.css
│
├── 📁 templates/                       # Jinja2 HTML templates
│   ├── login.html
│   ├── upload.html
│   └── result.html
│
├── 📄 app.py                           # Flask web server + routes
├── 📄 mri_segmentation.py             # U-Net inference pipeline
├── 📄 train.py                         # Model training script
├── 📄 requirements.txt                 # Python dependencies
├── 📄 LICENSE.txt                      # Proprietary license
└── 📄 README.md                        # This file

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🛠️ Installation & Quick Start

📋 Prerequisites

✓ Python 3.6+
✓ pip
✓ 64-bit OS (Windows 10 or Ubuntu)
✓ 4GB+ RAM (GPU recommended for training)

1️⃣ Clone

git clone https://github.com/Darkwebnew/Miniproject.git
cd Miniproject

2️⃣ Install Dependencies

pip install -r requirements.txt

Or manually:

pip install tensorflow==2.4.1
pip install opencv-python
pip install numpy
pip install scikit-learn
pip install flask

3️⃣ Run the Web App

python app.py

Open your browser at http://localhost:5000

4️⃣ (Optional) Retrain the Model

python train.py
# Trained model will be saved to h5/heart_mri_model.h5

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📊 Results & Performance

🎯 Segmentation Accuracy: 94.8%

Metric	Value
Segmentation Accuracy	94.8% ✅
Architecture	U-Net (Encoder-Decoder)
Input Size	256 × 256 px
Segments	Left Ventricle · Right Ventricle · Myocardium
Disease Classes	6 (Normal + 5 pathologies)
Framework	TensorFlow 2.4.1 / Keras
Model Size	heart_mri_model.h5

🌟 Clinical Impact

Benefit	Detail
⏱️ Speed	Hours of manual segmentation → sub-minute automated results
🎯 Precision	94.8% accuracy — comparable to expert radiologist consistency
👨‍⚕️ Clinical Value	Empowers cardiologists with reliable AI pre-screening
🏥 Workflow	Browser-based — integrates into any clinical environment
🔬 Research	Demonstrates deep learning's transformative role in cardiac imaging

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📋 Requirements

Category	Specification
OS	64-bit Windows 10 or Ubuntu
Python	3.6 or later
Deep Learning	TensorFlow 2.4.1
Image Processing	OpenCV
Numerics	NumPy, scikit-learn
Web Framework	Flask
IDE	VSCode or PyCharm (recommended)

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👥 Team

🏆 Core Development Team

Sriram V 🚀 Project Lead & Developer U-Net Architecture · Flask App · Model Training

Surothaaman R ⚙️ Backend Developer Flask Routes · Preprocessing · Integration

Andrew Varghese V S 🎨 Frontend & Research UI Templates · CSS Styling · Documentation

🎓 Academic Guidance

Role	Institution
Mini Project Supervisors	Saveetha Engineering College, Chennai

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🤝 Contributing

⚠️ Important: This project is under a restrictive proprietary license. Contributions are welcome strictly for educational improvement purposes only. By submitting a pull request, you agree your contribution becomes part of this project under the same license terms. No contributor may independently use, redistribute, or commercialize any part of this code.

How to Contribute

1. Open an Issue first — discuss your idea before coding
2. Fork the repository
3. Create a branch — git checkout -b feature/YourFeature
4. Commit — git commit -m 'feat: Add YourFeature'
5. Push & open a Pull Request with a detailed description

Contribution Areas

Area	Difficulty	Skills Needed
🧠 Model Improvements (new architectures)	Advanced	Python, TensorFlow, Keras
🌐 Web Interface Enhancement	Medium	Flask, HTML, CSS
📊 Additional Disease Classes	Advanced	Medical imaging, Deep learning
📚 Documentation	Beginner	Markdown
🧪 Evaluation Metrics (Dice, IoU)	Medium	Python, scikit-learn

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☕ Support the Project

If HeartSeg AI helped your research or clinical project — consider supporting continued development!

Your support helps build better AI healthcare tools for the community.

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📄 License

╔══════════════════════════════════════════════════════════════════╗
║              PROPRIETARY SOFTWARE LICENSE                        ║
║       Copyright (c) 2024–2025  Sriram V & HeartSeg AI Team      ║
║                   All Rights Reserved                            ║
╚══════════════════════════════════════════════════════════════════╝

This software and all associated source code, trained model weights, documentation, UI templates, screenshots, and assets are the exclusive intellectual property of the authors and are fully protected under applicable copyright law and the Indian Copyright Act, 1957.

❌ You MAY NOT:

• Copy, reproduce, or redistribute this code in whole or in part
• Use this project or any portion of it in commercial medical products or services
• Modify, adapt, or create derivative works based on this project
• Sublicense, sell, rent, or transfer rights to any third party
• Use this project's name, model weights, or research in your own publications without explicit written permission
• Deploy this system in any clinical, production, or commercial environment without written authorization
• Present this work as your own in academic or professional contexts

✅ You MAY:

• View and study the source code for personal educational purposes only
• Fork on GitHub solely to submit pull requests
• Reference this project in academic citations with proper attribution

⚖️ Legal Notice

Any unauthorized use, reproduction, distribution, or clinical deployment of this software is strictly prohibited and may result in civil and criminal penalties. The authors reserve all rights and will pursue all available legal remedies for any violations.

For licensing inquiries: @darkwebnew via GitHub Issues

See the full LICENSE.txt for complete terms.

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🙏 Acknowledgments

Technology	Purpose
TensorFlow / Keras	U-Net deep learning framework
OpenCV	Medical image preprocessing
Flask	Web server and routing
NumPy	Numerical computation
scikit-learn	Evaluation metrics
Saveetha Engineering College	Academic support and guidance
ACDC Dataset	Cardiac MRI benchmark reference

Academic References: Ronneberger et al. (U-Net, MICCAI 2015) · Bernard et al. (ACDC Challenge 2018)

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⭐ Star this repository if HeartSeg AI helped your project!

Made with ❤️ for advancing cardiac healthcare · Saveetha Engineering College · Tamil Nadu, India 🇮🇳

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