Attention-Guided EfficientNet-B3 with Grad-CAM Visualization for 22-Class Bone Fracture and Anatomical-Region...

Background/Objectives: To achieve effective clinical decision-making from radiographic images, accurate diagnosis of bone fractures is required, but this is difficult due to anatomical variation, subtle fracture appearance, and variable imaging conditions. Existing deep learning studies for fracture detection are predominantly limited to binary classification or single anatomical regions, limiting their real-world clinical utility. This study demonstrates that a single deep learning model can effectively classify multi-region bone fractures by transforming the task into a 22-class classification problem, utilizing the publicly available MultiBoneX dataset. Methods: The proposed model utilizes an EfficientNet-B3 convolutional neural network integrated with a Convolutional Block Attention Module (CBAM) to enhance feature representation by focusing on diagnostically significant areas. We used regularized preprocessing, data augmentation, and structured training to support strong model learning and generalization. Model predictions were interpreted using Gradient-weighted Class Activation Mapping (Grad-CAM) to highlight the image regions that were most important for class selection. Results: When tested on a held-out test set of 3280 images, the model achieved an overall accuracy of 75.03% (95% CI: 73.57-76.43%), with precision, recall, and F1-score of 77.09% (95% CI: 75.28-78.77%), 72.85% (95% CI: 71.00-74.60%), and 73.45% (95% CI: 71.49-75.15%), respectively. The overall multi-class Matthews Correlation Coefficient (MCC) was 0.7361, providing an additional class-imbalance-aware measure of classification performance. Conclusions: These findings demonstrate the feasibility of a unified, multi-class system for diagnosing bone fractures across diverse anatomical sites, providing a scalable foundation for future AI-assisted radiography.