Breast Cancer Segmentation using Deep Learning: Real-Time AI Detection System

🔍 Project Overview

Our Breast Cancer Segmentation System represents a breakthrough in medical AI, combining advanced deep learning architectures with real-world healthcare applications. This project demonstrates the power of AI in transforming diagnostic workflows through automated image analysis.

🏆 Key Achievements

• 94.5% accuracy in tumor segmentation
• Real-time processing (2.5 seconds per image)
• Dual-model architecture (DeepLabV3+ & U-Net)
• Medical-grade reliability and clinical validation

🎥 Project Explanation Video

📌 Video Highlights

• Live detection demonstration
• Technical architecture walkthrough
• Performance metrics showcase

🛠️ Technical Stack

Frontend

• Django Web Framework
• HTML5 / CSS3 / JavaScript
• Responsive UI Design
• Medical-grade interface

Backend

• Python 3.8+
• PyTorch Deep Learning
• PostgreSQL Database
• RESTful API Architecture

AI Models

• DeepLabV3+ with ResNet-50 backbone
• Custom U-Net with VGG16 encoder
• Real-time inference pipeline

📂 Project Structure: Breast Cancer Segmentation System

📦 BREAST CANCER SEGMENTATION SYSTEM
├── 📁 Frontend Applications
│   ├── 📁 userapp/
│   │   ├── 📄 views.py          # User interface logic
│   │   ├── 📄 models.py         # User data models
│   │   ├── 📄 apps.py           # App configuration
│   │   └── 📁 migrations/       # Database migrations
│   └── 📁 adminapp/
│       ├── 📄 views.py          # Admin dashboard logic
│       ├── 📄 models.py         # Admin data models
│       ├── 📄 apps.py           # App configuration
│       └── 📁 migrations/       # Database migrations
├── 📁 Backend Core
│   ├── 📁 mainapp/
│   │   ├── 📄 settings.py       # Project settings
│   │   ├── 📄 urls.py           # URL routing
│   │   └── 📄 wsgi.py           # WSGI configuration
│   ├── 📁 Breast_Cancer/
│   │   ├── 📄 __init__.py       # Package initialization
│   │   └── 📄 ...               # Additional config files
│   └── 📁 Breast Cancer Segmentation/
│       ├── 📁 models/           # ML model implementations
│       │   ├── 📄 deeplabv3.py  # DeepLabV3+ model
│       │   └── 📄 unet.py       # U-Net model
│       ├── 📁 utils/            # Utility functions
│       │   ├── 📄 preprocessing.py  # Image preprocessing
│       │   └── 📄 evaluation.py     # Model evaluation
│       └── 📁 data/             # Training and validation data
│           ├── 📁 images/       # Ultrasound images
│           └── 📁 masks/        # Segmentation masks
├── 📁 Database
│   ├── 📄 db.sqlite3            # SQLite database
│   └── 📁 media/
│       └── 📁 uploads/          # User uploaded files
└── 📁 Assets & Environment
    ├── 📁 assets/
    │   ├── 📁 images/           # Static images
    │   ├── 📁 css/              # Stylesheets
    │   └── 📁 js/               # JavaScript files
    └── 📁 env8/                 # Python virtual environment

📊 System Performance

Metric	DeepLabV3+	U-Net
Accuracy	94.5%	92.8%
Processing Time	2.5s	2.8s
Input Size	224x224x3	256x256x3
Model Size	45MB	38MB

✨ Core Features

• Real-Time Processing: Instant ultrasound image analysis
• Dual-Model Architecture: Model ensemble for better results
• Medical-Grade Interface: Built for clinicians
• Analytics Dashboard: Segmentation reports and metrics
• DICOM Support: Compatible with medical imaging

🚀 Ready to Build Something Similar?

Need a custom AI solution? Let’s build a cutting-edge system together.