FracNet: An end-to-end deep learning framework for bone fracture detection

Abstract

Fracture detection in medical imaging is crucial for accurate diagnosis and treatment planning in orthopaedic care. Traditional deep learning (DL) models often struggle with small, complex, and varying fracture datasets, leading to unreliable results. We propose FracNet, an end-to-end DL framework specifically designed for bone fracture detection using self-supervised pretraining, feature fusion, attention mechanisms, feature selection, and advanced visualisation tools. FracNet achieves a detection accuracy of 100% on three datasets, consistently outperforming existing methods in terms of accuracy and reliability. Furthermore, FracNet improves decision transparency by providing clear explanations of its predictions, making it a valuable tool for clinicians. FracNet provides high adaptability to new datasets with minimal training requirements. Although its primary focus is fracture detection, FracNet is scalable to various other medical imaging applications. • Propose an end-to-end deep learning framework for accurate bone fracture detection. • Incorporate attention mechanisms to enhance deep network performance. • Use feature fusion and selection to improve representation and model robustness. • Evaluate on three datasets, outperforming state-of-the-art methods. • Validate single classifier generalisation across multiple datasets successfully.

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