Patch-based feature mapping with generative adversarial networks for auxiliary hip fracture detection

Incorporating PAGAN improved the performance of the classification models. The accuracy of EfficientNetB0 increased from 93.61 % to 95.97 %, ResNet50 improved from 90.66 % to 94.89 %, and DenseNet121 saw an increase from 93.51 % to 94.49 %. Regarding model explainability, the integration of PAGAN into classification models led to a more pronounced attention to ROI. The average IOU improved from 0.32 to 0.54 for EfficientNetB0, from 0.28 to 0.40 for ResNet50, and from 0.37 to 0.51 for DenseNet121. These results indicate that PAGAN improves hip fracture classification performance and substantially enhances the model's focus on the fracture region, thereby increasing its explainability.