Bone Fracture Detection Using X-Ray Images DMML2: Project Report Author: Akimuddin Aslam Shaikh and Sanjay Girish Dialani Institution: National College of Ireland, Dublin, Ireland

📌 Project Overview This project aims to develop an automated bone fracture detection system using machine learning and deep learning techniques. It focuses on analyzing X-ray images to diagnose fractures efficiently and accurately, assisting healthcare professionals in making informed decisions.

🔍 Key Features ✅ Implemented Convolutional Neural Networks (CNNs) for deep learning-based feature extraction. ✅ Applied Canny Edge Detection combined with Random Forest for classical machine learning-based fracture detection. ✅ Used Transfer Learning with a pre-trained EfficientNetB3 model to improve accuracy. ✅ Followed the CRISP-DM methodology for data preprocessing, feature engineering, and evaluation. ✅ Achieved high accuracy (94.59%) with EfficientNetB3 and Edge Detection, improving diagnostic reliability.