Bone Fracture Detection Using Deep Learning: A Review

Abstract

Bone fracture detection is important for clinical diagnostics and emergency medicine around the world. Manual interpretation of X-rays takes a lot of time, is prone to mistakes, and relies on the availability of experts. Deep Learning (DL) has become an effective solution, showing impressive results in visual recognition tasks. Convolutional Neural Networks (CNNs) and Transformer-based architectures, like Vision Transformers (ViT), have changed the game for automated feature extraction and image understanding. This review paper provides an overview of DL and Transformer applications in bone fracture detection using X-ray images. We examine recent advancements from 2018 to 2024, discussing datasets, preprocessing, model architectures, evaluation metrics, and hybrid frameworks that combine CNNs and Transformers. Comparative analyses show that hybrid models offer better contextual and spatial learning, outperforming traditional machine learning methods. Lastly, we address challenges such as data scarcity, interpretability, and computational demands. We also look at future directions that highlight explainable AI, multimodal learning, and real-time clinical applications.

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