Performance Evaluation of Deep Learning Applications in Calcaneal Fracture Diagnosis Using Separate Radiographic Views: A Comparison of CNN and Vision Transformer Architectures

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Abstract
Background: Calcaneal fractures are challenging to diagnose on routine radiographs and may result in missed diagnoses. Objective: This study compares Convolutional Neural Networks (CNNs) and Vision Transformer (ViT) architectures for detecting calcaneal fractures separately in three standard radiographic views: Anteroposterior (AP), Harris (axial), and Lateral. Methods: A balanced retrospective dataset of 178 patients with complete three-view examinations (89 with displaced calcaneal fractures and 89 controls) was used to evaluate five deep learning models: ResNet50, InceptionV3, InceptionResNetV2, XCiT, and PiT. After image-quality screening, each view underwent an approximately 8:2 image-level training–validation split; no independent test set was used. Images were preprocessed using data augmentation and Contrast Limited Adaptive Histogram Equalization (CLAHE), while Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to visualize model activation. Results: InceptionV3 achieved 100% sensitivity in the AP view, while ResNet50 achieved 100% specificity in both AP and Lateral views. XCiT matched ResNet50 across all five classification metrics in the AP view and matched ResNet50 and InceptionV3 in the Lateral view. PiT achieved 100% accuracy in the Harris validation set (95% CI, 89.0–100.0%). Grad-CAM visualizations showed activation over the calcaneal region in representative fracture images. Conclusions: This study demonstrates the feasibility of deep learning for identifying displaced calcaneal fractures through separate evaluations of AP, Harris, and Lateral radiographs. The model-specific performance patterns provide a foundation for further development of AI-assisted calcaneal fracture detection and future investigation of multi-view integration.
Funding Information
  • Taichung Veterans General Hospital (TCVGH-PU1138104)

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