Optimizing Diabetic Foot Ulcer Classification with Transfer Learning: A Performance Analysis

Diabetic foot ulcers (DFUs) are prolonged wounds that commonly affect individuals with diabetes, often resulting from a combination of neuropathy and poor circulation. These ulcers pose significant health risks and can lead to severe complications, including infection and amputation, if not properly managed. This paper presents an advanced approach to diabetic foot ulcer (DFU) classification and recognition using transfer learning models. This study evaluates the performance of various transfer learning models in the classification of Diabetic Foot Ulcers (DFUs), focusing on their accuracy, specificity, and sensitivity. The models assessed include ResNet152, EfficientNetB7, and SE-ResNeXt. EfficientNetB7 achieved the highest accuracy at 99.65% and sensitivity at 99.8%, indicating its superior capability in accurately identifying DFU cases and true positives. ResNet152, with an accuracy of 99.39%, demonstrated the highest specificity at 99.8%, effectively minimizing false positives. SE-ResNeXt, while slightly lower in performance metrics, still provided robust classification capabilities. These results highlight the efficacy of transfer learning models in medical image classification, emphasizing their potential to improve diagnostic accuracy and patient outcomes in DFU detection.