DFU-MambaKAN: A Lightweight Hybrid Mamba-KAN Architecture for Diabetic Foot Ulcer Screening and Severity Grading

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Abstract
Diabetic foot ulcers (DFUs) are a leading cause of lower-extremity amputation world wide, yet routine assessment still relies on manual visual inspection by trained clinicians, a resource that is subjective and unevenly available in low-resource settings. We pro pose DFU-MambaKAN,alightweight hybrid architecture that combines a pure-Py Torch selective state-space (Mamba) block with a Gaussian radial-basis-function Kolmogorov Arnold Network (RBF-KAN) feed-forward layer. The model targets two clinically moti vated tasks organized as a screening-then-grading cascade: (1) binary normal-versus-ulcer screening and (2) four-class Wagner-Meggitt severity grading. It uses only 1.06 million parameters, 1.4 to 22 times fewer than four standard baselines (ResNet50, Efficient Net B0, MobileNetV3-Small, ViT-Tiny) evaluated under an identical, deduplicated, fixed-seed train/validation/test protocol. On the screening task, DFU-Mamba KAN reaches 97.17% accuracy (macro-F1 0.970, AUC 0.994), within 0.9–2.9 points of all four baselines. On the severity-grading task it reaches 67.75% accuracy (macro-F1 0.677, AUC 0.895), a much larger gap than the baselines (98.1–99.0%), which we analyze in detail and attribute mainly to under-convergence of the sequential state-space scan on a problem with ten times more data and twice as many classes, rather than to a fundamental limitation of the architec- ture. We also checked the two public Kaggle-hosted DFU datasets used here for duplicate images and found a 32.4% exact-duplicate rate in one of them; left uncorrected, this in- flates reported accuracy through train/test leakage. The main contribution of this paper is not a state-of-the-art accuracy claim. It is a reproducible, efficiency-aware benchmark, a parameter-efficient architecture, and a reported limitation, together with concrete next steps toward low-cost, edge-deployable DFU triage tools.

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