Efficient Diabetic Foot Ulcer Classification: A Comparative Study of Derivative-Free Methods for Resource-Constrained Clinical Environments

Mohammed A. B. Mahmoud

Abstract: Diabetes affects over 820 million people globally, as reported by theWorld Health Organization. A significant complication of diabetes is diabetic foot ulcers (DFUs), which necessitate early detection to prevent severe outcomes, including lower limb amputation. This study proposes a method comparing the performance of gradient-based and non-gradient-based approaches for DFU analysis. Specifically, we evaluate a gradient based vision transformer (ViT) against two non-gradient methods: the Cascaded Forward (CaFo) algorithm and the Pseudoinverse Learning autoencoder (PILAE). ViT, a backbone of many recent large language models (LLMs), relies on the backpropagation (BP) algorithm during training. However, BP is associated with several limitations, such as vanishing/exploding gradients, overfitting, local minima, architectural constraints, and high computational demands. With an emphasis on clinical deployability, this work offers the first thorough comparison of gradient-free techniques (PILAE and CaFo) versus a cutting-edge gradient-based Vision Transformer for DFU classification. Our findings show that PILAE offers a workable, affordable alternative for early DFU identification in resource constrained contexts by achieving competitive diagnostic performance with noticeably decreased computing overhead.