Explainable and clinically audited hybrid self-supervised framework for multi-class DFU classification and mobile deployment

Accurate and explainable classification of diabetic foot ulcers (DFUs) is vital for early intervention and patient management. This paper presents a Clinically Audited Hybrid Self-Supervised Framework integrating domain-adaptive SimCLR pretraining, a U-Net-based refiner, and an EfficientNet-B0 classifier for four-class DFU diagnosis (Normal, Abnormal, Ischemic, Infected) by fusing two DFU datasets. The proposed method leverages self-supervised representation learning to capture ulcer-specific texture and perfusion cues, while the U-Net refiner enhances lesion boundary visibility for improved downstream feature extraction. Experiments were conducted on an aggregated DFU dataset of 24,000 images, partitioned into 19,200 for training, 1200 for validation, and 3600 for testing (80/5/15 split). The model achieved an accuracy of 99.98%, precision of 99.91%, recall of 99.92%, and F1-score of 99.93%, with an external clinician-audited macro F1 of 0.9993 and Cohen's Kappa of 0.985. Explainability assessment using Grad-CAM yielded a mean IoU of 0.982 ± 0.004 with 87.5% clinical interpretability confirmed by experts. The mobile deployment (TensorFlow Lite) achieved an inference latency of 180 ms/image and a total app footprint of ~ 150 MB, supporting real-time analysis on mid-range hardware. The proposed framework demonstrates that hybrid self-supervised pretraining with spatial refinement can achieve high diagnostic precision, strong clinical interpretability, and efficient mobile deployment, advancing the feasibility of AI-driven DFU screening in telemedicine and homecare environments.