An AI-driven framework for diabetic foot ulcer classification, segmentation, and depth estimation

Diabetic Foot Ulcers are a severe complication of diabetes that can lead to infection, amputation, and increased mortality, making timely and objective assessment essential. Conventional evaluation relies primarily on visual inspection and manual measurements, which are often subjective and inconsistent. This paper presents a comprehensive automated deep learning-based framework for DFU assessment that integrates classification, segmentation, relative depth estimation, and explainability. An EfficientNet-B0 model is employed for binary classification of ulcerated versus healthy skin. At the same time, a U-Net with an EfficientNet-B0 encoder is used for precise delineation of ulcer boundaries, enabling quantitative morphometric analysis such as area and width estimation. Model interpretability is incorporated through Gradient-weighted Class Activation Mapping and Local Interpretable Model-Agnostic Explanations, providing visual insights into the decision-making process. To characterize wound topology, relative pseudo-depth maps are generated from single RGB images using a MiDaS-based monocular depth estimation approach. Finally, an automated reporting module synthesizes outputs from all components into structured, clinician-friendly summaries. Experimental results demonstrate strong classification performance, accurate segmentation, and meaningful characterization of relative depth, highlighting the potential of the proposed framework as a research-oriented AI-assisted decision-support tool for DFU assessment. However, further clinical validation involving expert clinicians and prospective studies is required before real-world clinical deployment.