TransUNet-GradCAM: A Hybrid Transformer-U-Net with Self-Attention and Explainable Visualizations for Foot Ulcer Segmentation

Automated segmentation of diabetic foot ulcers (DFUs) supports clinical diagnosis, treatment planning, and wound monitoring, but remains challenging because of heterogeneous appearance, irregular morphology, and cluttered backgrounds in clinical photographs. We evaluate a hybrid ViT-bottleneck U-Net that combines a convolutional encoder-decoder with a Transformer bottleneck and attention-gated skip connections. We emphasise rigorous validation and explainability rather than architectural novelty. The model was trained on the public Foot Ulcer Segmentation Challenge (FUSeg) dataset using a hybrid Dice and cross-entropy loss. Results are reported over five seeds as mean +/- 95% confidence interval at a fixed threshold. On the internal validation set, the model achieved a Dice of 0.8035 +/- 0.0053 and IoU of 0.7149 +/- 0.0073 (HD95 = 19.74 px, ASSD = 6.12 px). Ablation showed that only the hybrid loss significantly changed Dice (-0.038, p < 0.001), while the Transformer bottleneck, attention gates, and augmentation had small, non-significant in-domain effects. External validation without retraining achieved a Dice of 0.7460 on the AZH Wound Care Center cohort (n = 278), retaining about 92% of internal Dice. A small Medetec subset (n = 8) was used only for qualitative assessment, indicating partial rather than robust generalisation under domain shift. Explainability analysis found Grad-CAM more wound-localised (energy-in-mask 0.871 vs. 0.102), while attention rollout was significantly more faithful (p = 0.038, n = 200). Predicted and expert wound areas showed strong agreement (Pearson r = 0.944), with a lightweight model of 8.79 M parameters.