An Integrated Deep Learning Framework for Automated Leg Bone Fracture Detection and Clinical Decision Support Using X-Ray Images

Abstract

Leg bone fractures require accurate and timely diagnosis to determine appropriate treatment. This research proposes an Artificial Intelligence (AI) based deep learning framework for automated leg bone fracture detection and severity assessment using X-ray images. Initially, a Median Filter is applied as a preprocessing technique to remove noise and enhance fracture-related structures. The preprocessed images are then analyzed using Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) algorithms for feature extraction, fracture detection, and classification. The proposed system identifies fractured and non-fractured bones and assesses the severity or stage of the fracture. Based on the detected fracture characteristics, the system provides computer-aided treatment recommendations, including conservative management, supportive medication, or surgical intervention when clinically appropriate. The proposed framework aims to improve fracture detection accuracy, reduce diagnostic time, and support healthcare professionals in making effective treatment decisions.