A transfer learning–based approach for automated bone fracture classification in X-ray imaging

Background: Bone fractures present a significant diagnostic challenge in medical imaging, necessitating accurate and automated classification methods. Recent advancements in deep learning have greatly enhanced the diagnostic precision while reducing human error. Objectives: This study proposes an ensemble deep learning model, EnsembleAttenBoneNet, that integrates fine-tuned ResNet50 and EfficientNetB3 models augmented with a Squeeze-and-Excitation (SE) attention mechanism, for robust classification of bone fractures in X-ray images. Design: The dataset consists of ten distinct fracture categories, such as avulsion, comminuted, greenstick, and pathological fractures. Methods: Preprocessing techniques, including resizing, normalization, and augmentation, have been applied to improve generalization. Features extracted from both networks were concatenated and refined using the SE attention module to enhance feature representation. Results: The proposed model achieved a classification accuracy of 99.48%, outperforming the individual models (EfficientNetB3: 98.56%, ResNet50: 97.86%). Conclusion: Experimental results affirm that integrating deep learning models with attention mechanisms significantly improve diagnostic accuracy, rendering the model a valuable tool for clinical fracture detection. Future research will investigate dataset extension and conduct real-world validation to enhance its usability in medical imaging.