A Computer Aided Diagnostic System for Bone Fracture Detection in X-Ray Images

From Deep Learning Models to Clinical Deployment.

Abstract

Bone fractures are among the most common musculoskeletal injuries, yet X-ray interpretation remains susceptible to human error, particularly in regions facing radiologist shortages. This paper presents the development and clinical deployment of an end-to-end Computer-Aided Diagnosis (CAD) system for automated binary bone fracture detection. We systematically evaluate four custom Convolutional Neural Network (CNN) architectures and two transfer learning models (EfficientNetB0 and VGG16) on a public musculoskeletal X-ray dataset (Kaggle: ahmedashrafahmed/bone-fracture, version 1). To mitigate overfitting and improve robustness, an aggressive data augmentation pipeline was applied to the training set. Experimental results show that transfer learning significantly outperforms custom architectures, with EfficientNetB0 achieving the strongest diagnostic performance: 93.8% accuracy, 0.96 AUC-ROC, and a clinically critical 92.7% sensitivity. To translate these findings into practical clinical utility, the optimal model was integrated into a secure, lightweight Flask-based web application featuring real-time inference, strict input validation, UUID-based file anonymization, and automatic post-inference data deletion for compliance with patient-privacy standards.