An Interpretable Deep Learning Framework for Diabetic Foot Ulcer Classification Using Multi Optimizer Training and Genetic Algorithm Selection

This research introduces an interpretable deep learning framework for diabetic foot ulcer (DFU) classification by integrating a custom CNN architecture with multi-optimizer training, Genetic Algorithm (GA)-based model selection, and Grad-CAM explainability. Unlike conventional approaches that rely on manually selected optimizers and black-box predictions, the proposed framework systematically evaluates seven optimizers and automatically selects the best-performing model using evolutionary optimization. The selected AdamW-based CNN achieved 95.24% accuracy and 98.97% ROC-AUC on an independent test set while providing visual explanations through Grad-CAM to highlight clinically relevant ulcer regions. The framework offers a lightweight, accurate, and transparent AI solution to support reliable DFU screening and clinical decision-making.