FracNet:A Dual-Path Deep Learning Framework for Multi-Scale Hip Fracture Classification from X-rays

In this study, we present a deep learning framework for multi-class classification of hip fractures using plain radiographic images. The proposed architecture integrates a ResNet50 backbone with a Feature Pyramid Network to extract multi-scale features, and introduces a dual-path block that combines convolutional and transformer-based mechanisms to capture both local and global information. A multilevel fusion module is used to refine and integrate features from different stages. The model was trained and evaluated on a clinically annotated dataset from Chosun University Hospital, demonstrating superior performance over several state-of-the-art baselines.