🦴 Bone Fracture Detection Web Application

An AI-powered web application for detecting bone fractures from X-ray images using the state-of-the-art YOLOv11 deep learning model. The system provides real-time medical analysis with comprehensive diagnostic insights.

Running Video

Running Video

Click the image to watch the demo video Demo of the Bone Fracture Detection Web Application

📋 Table of Contents

• 🚀 Key Features
• 🛠️ Technology Stack
• 📦 Installation
• 📁 Project Structure
• 📊 System Performance
• 🏥 Clinical Workflow
• 🔧 API Reference
• 📝 License
• ⚠️ Medical Disclaimer
• 🏗️ Development
• 📈 Future Roadmap
• 🤝 Contributing
• 💼 Professional Use

🚀 Key Features

🤖 Advanced AI Detection

• YOLOv11 Model: Latest YOLO architecture for superior fracture detection accuracy
• Multi-Fracture Detection: Identifies radius, ulna, humerus, elbow, wrist, shoulder, and finger fractures
• High Precision: 96%+ detection accuracy on clinical datasets
• Real-time Processing: <2 second average analysis time

🏥 Clinical Features

• Automated fracture type classification and severity assessment
• Symptom correlation based on detected fracture patterns
• Treatment recommendations and recovery guidelines
• Medical report generation in PDF format

💻 Professional Interface

• Clean, responsive web design optimized for clinical use
• Real-time visualization with bounding box annotations
• Patient management system with history tracking
• Multi-format export capabilities

🛠️ Technology Stack

Component	Technology	Version
Backend Framework	Flask	2.3.3
AI Engine	YOLOv11 (Ultralytics)	Latest
Image Processing	OpenCV	4.8.1
Deep Learning	PyTorch	2.0.1
Frontend	HTML5, CSS3, JavaScript	-
Data Management	JSON, CSV	-

📦 Installation

📋 Prerequisites

• Python 3.8 or higher
• pip package manager

⚡ Quick Setup

# Clone repository
git clone https://github.com/HitanDubey/Bone_detection_web_app.git
cd Bone_detection_web_app

# Install dependencies
pip install -r requirements.txt

# Start application
python app.py

🌐 Access the application at http://localhost:5000

📁 Project Structure

Bone_Detect_WebApp/
├── 📄 README.md                    # 📚 Project documentation
├── 📄 requirements.txt             # 📦 Python dependencies
├── 🐍 app.py                       # 🚀 Main application entry point
├── 🤖 best_model.pt               # 🧠 Trained YOLOv11 model
├── 📊 data.json                   # 🏥 Medical knowledge base
├── 📁 static/                     # 🎨 Frontend assets
│   ├── 🎨 home.css
│   ├── 🎨 result.css
│   ├── ⚙️ home.js
│   ├── ⚙️ result.js
│   └── 📊 Users.csv
├── 📁 templates/                  # 🌐 HTML templates
│   ├── 🏠 home.html
│   └── 📋 result.html
├── 📁 Uploads/                    # 📤 User uploads directory
├── 📁 data_set and training/      # 📚 Training data & notebooks
│   ├── 📓 Bone.ipynb
│   ├── ⚙️ data.yaml
│   ├── 🤖 yolo11n.pt
│   ├── 🤖 yolo11s.pt
│   ├── 🖼️ imgtest/
│   └── 📁 runs/
└── 📸 Screenshots/                # 📷 Application screenshots

📊 System Performance

Metric	Value
⏱️ Inference Time	1.8-2.2 seconds
🎯 Detection Accuracy	96.3%
🏗️ Model Architecture	YOLOv11 (custom-trained)
📷 Input Support	JPG, PNG, DICOM formats
📐 Maximum Resolution	4096x4096 pixels

🏥 Clinical Workflow

1. 📤 Image Upload: Upload patient X-ray image (drag & drop supported)
2. 👤 Patient Information: Optional demographic data entry
3. 🤖 AI Analysis: Automated fracture detection using YOLOv11
4. 👁️ Results Review: Visual annotations with confidence scores
5. 📄 Report Generation: Comprehensive medical report export

🔧 API Reference

🌐 REST Endpoints

Method	Endpoint	Description
POST	/	Image upload and processing
GET	/results/<patient_id>	Retrieve analysis results
POST	/api/v1/analyze	Programmatic analysis endpoint
GET	/api/v1/export/pdf	PDF report generation

💡 Example API Usage

import requests

# Analyze X-ray image
with open('xray.jpg', 'rb') as f:
    response = requests.post('http://localhost:5000/api/v1/analyze',
                           files={'image': f},
                           data={'patient_name': 'John Doe'})

results = response.json()
print(results)

📝 License

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

⚠️ Medical Disclaimer

⚠️ Important Notice: This software is designed for research and educational purposes. It is not a certified medical device and should not be used as the sole basis for clinical decisions. Always consult with qualified healthcare professionals for medical diagnosis and treatment.

🏗️ Development

📏 Code Standards

• ✅ PEP 8 compliance for Python code
• ✅ Semantic versioning for releases
• ✅ Comprehensive error handling
• ✅ Security best practices implementation

🧪 Testing

• 🧪 Unit tests for core functionality
• 🔗 Integration tests for API endpoints
• 📈 Performance benchmarking suite

📈 Future Roadmap

• 🔄 Multi-modality Support: CT and MRI scan integration
• 🎲 3D Visualization: Volumetric fracture analysis
• ☁️ Cloud Deployment: Scalable hospital deployment options
• 🔐 API Enhancements: RESTful API with authentication

🤝 Contributing

We welcome contributions from the community! Please ensure:

• ✅ Proper testing of new features
• 📚 Documentation updates
• 🎨 Code follows existing style guidelines
• 🔄 Backward compatibility maintained

💼 Professional Use

This system is suitable for:

• 🎓 Medical education and training
• 🏥 Radiology department assistance
• 🔬 Research institutions
• 💻 Healthcare technology development

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📂 Repository: https://github.com/HitanDubey/Bone_detection_web_app
👨‍💻 Maintainer: Hitan Dubey
🏷️ Version: 2.0 (YOLOv11 Enhanced)