DFU-GCNet: a global context-enhanced inception network for robust and interpretable diabetic foot ulcer classification.

Introduction: Diabetic foot ulcers (DFUs) are severe complications that cause frequent lower extremity amputations. Timely diagnosis is crucial for effective clinical management. Although deep learning approaches improve detection, the models often struggle to capture different lesion scales. Furthermore, opaque algorithmic decisions often lower medical trust. Therefore, this study introduces DFU-GCNet for robust and interpretable ulcer classification. Methods: The proposed architecture merges inception modules with global context blocks. This combination extracts multi-scale features from different wound sizes and simultaneously models broad spatial dependencies across tissue regions. Thus, it effectively distinguishes pathology from surrounding healthy skin. We evaluate this framework using the Kaggle DFU dataset. We integrate explainable AI techniques to ensure clinical transparency. GradCAM++, Local Interpretable Model-Agnostic Explanations, and SHapley Additive exPlanations are used to provide high-resolution diagnostic heatmaps and confirm that the network prioritizes clinically relevant wound boundaries. Results: The model achieved a superior classification accuracy of 97.16%, with an F1-score of 0.9715 and a Matthews correlation coefficient of 0.9437. DFU-GCNet demonstrated decisive superiority compared with standardized modern baselines such as VGG16 and EfficientNet. Discussion: The findings indicate that DFU-GCNet is a highly reliable automated screening instrument.