Transfer Learning in Bone Fracture Detection: A Comprehensive Review

Abstract

Bone fractures are prevalent musculoskeletal injuries whose timely and accurate diagnosis is critical for effective treatment. Over the past decade, deep learning has demonstrated substantial potential in automated fracture detection, with convolutional neural networks (CNNs) often outperforming traditional diagnostic approaches. This review synthesizes recent research, emphasizing the application of transfer learning, wherein pre-trained models are adapted to overcome limited and heterogeneous clinical datasets. Various deep architectures, including ResNet, VGG16, AlexNet, and MobileNet, are examined—alongside discussions on both binary and multi-class classification strategies—to enhance model robustness and capture the full spectrum of fracture types. The review also highlights the role of explainable AI in bolstering clinician confidence by elucidating network decision-making processes. Despite these advances, challenges persist, such as small sample sizes, inconsistent imaging protocols, and insufficient clinical validation. Future endeavors should explore multi-modal image integration, standardize evaluation metrics, and conduct large-scale, real-world assessments. By addressing these issues, transfer learning-driven methodologies can provide more reliable, efficient, and interpretable diagnostic tools, ultimately improving patient care.

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