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.

Diabetic foot ulcers (DFUs) are a major cause of lower-extremity amputation, and image-based decision support may assist screening and preliminary triage where specialist access is limited. We propose DFU-MambaKAN, a hybrid architecture combining a pure-PyTorch selective state-space (Mamba) block with a Gaussian radial-basis-function Kolmogorov–Arnold Network (RBF-KAN) feed-forward layer. The study evaluates two distinct tasks: (1) binary normal-versus-ulcer screening and (2) a dataset-specific four-class Wagner-Meggitt severity-grading task. DFU-MambaKAN contains 1.062 million parameters and was compared with ResNet50, EfficientNet-B0, MobileNetV3-Small, and ViT-Tiny under the manuscript’s common downstream training protocol. The reported values are point estimates from one deterministic 70/15/15 split generated with seed 42; no confidence intervals, repeated-seed averages, or inferential significance tests were obtained. On binary screening, DFU-MambaKAN achieved 97.17% accuracy, macro-F1 0.970, and AUC 0.994. On four-class grading, it achieved 67.75% accuracy, macro-F1 0.677, and AUC 0.895, whereas the baselines achieved 98.14–99.00% accuracy. This large gap means that the present evidence does not establish competitiveness for multiclass severity grading. Possible contributors include training duration and convergence, dataset size and class definitions, label or image-quality uncertainty, hyperparameter selection, global token mixing, and architecture–task mismatch; extended learning-curve analysis and multi-seed evaluation are needed to test these explanations. An automated technical audit identified 342 exact duplicates among 1055 class-folder images in Dataset A (32.4%); no deduplicated-versus-nondeduplicated performance comparison was performed, so this finding is reported as a methodological caution rather than proof of accuracy inflation. The model’s 1.062M parameter count supports parameter-efficient storage, but its measured single-image GPU latency was 90.54–93.14 ms, substantially higher than the baselines. Accordingly, the current evidence supports further investigation for batched or queued screening more strongly than immediate real-time mobile deployment. External clinical validation, multi-seed analysis, statistical testing, convergence studies, and component ablations remain necessary.