Bone Fracture Detection - Computer Vision Project

Complete computer vision pipeline for bone fracture detection with three modules: Image Processing, Classical Features, and Deep Learning.

Project Structure

Core Implementation

• complete_all_modules.py - Main pipeline (All 3 modules integrated)
• src/bonefracture/ - Source code package
• train_yolo_proper.py - YOLO training script
• realtime_yolo_detection.py - Real-time detection
• segnet.py - Segmentation model

Training on Colab

• colab_yolo_training.ipynb - YOLO training notebook (5-10 epochs)
• colab_setup.ipynb - General Colab setup

Reports

• research_reports/ - Module reports (Required)
• results/ - Final reports

Quick Start

Local Training

pip install -r requirements.txt
python complete_all_modules.py

Colab Training (Recommended)

1. Open colab_yolo_training.ipynb in VS Code
2. Install Colab extension
3. Connect to Colab and select GPU runtime
4. Run cells to train YOLO model

Real-Time Detection

python realtime_yolo_detection.py --source webcam --model path/to/model.pt

Project Requirements

See PROJECT_REQUIREMENTS_ANALYSIS.md for complete requirements compliance.

Documentation

• PROJECT_COMPLETE_GUIDE.md - Complete project guide with all modules, code files, and line counts
• PROJECT_REQUIREMENTS_ANALYSIS.md - Requirements compliance analysis

Models

• DenseNet-121: Classification (74.56% accuracy)
• YOLOv8: Object detection (7 fracture types)
• SegNet: Segmentation

GPU Requirements

• Minimum: 4 GB VRAM (YOLOv8n)
• Recommended: 8 GB VRAM (YOLOv8s)
• Optimal: 16+ GB VRAM (YOLOv8m/l)

For training, use Google Colab (free GPU) or local GPU.