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Related Experiment Video

Updated: Jun 25, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Diabetic Foot Ulcers Detection Model Using a Hybrid Convolutional Neural Networks-Vision Transformers.

Abdul Rahaman Wahab Sait1, Ramprasad Nagaraj2

  • 1Department of Archives and Communication, Center of Documentation and Administrative Communication, King Faisal University, P.O. Box 400, Hofuf 31982, Al-Ahsa, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|March 28, 2025
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Diabetic Foot Ulcer01:31

Diabetic Foot Ulcer

Definition A diabetic foot ulcer (DFU) is a chronic, non-healing wound that develops in individuals with diabetes. It typically occurs on pressure-bearing areas such as the heel, metatarsal heads, or hallux, and carries a high risk of infection and amputation.Pathophysiology • The development of DFUs can be explained by four interconnected mechanisms: neuropathy, ischemia, infection, and impaired wound healing. • Neuropathy is the most common factor. Sensory neuropathy reduces pain perception,...

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This study introduces an advanced AI model for classifying diabetic foot ulcers (DFUs), achieving high accuracy and interpretability. The model enhances early diagnosis and treatment strategies for diabetic foot complications.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Diabetology

Background:

  • Diabetic foot ulcers (DFUs) are a serious diabetes complication requiring precise classification for effective management.
  • Current DFU classification methods face challenges in performance, generalization, and interpretability, limiting clinical utility.

Purpose of the Study:

  • To develop an innovative, robust, and interpretable model for classifying diabetic foot ulcer severity.
  • To address the limitations of existing DFU classification techniques in clinical settings.

Main Methods:

  • Integration of deep learning architectures (MobileNet V3-SWIN, LeViT-Peformer) with tensor-based feature fusion.
  • Application of ensemble splines-based Kolmogorov-Arnold Networks (KANs) combined with Shapley Additive exPlanations (SHAP) for interpretability.
Keywords:
Kolmogorov–Arnold networksdeep learningdiabetic mellitusfeature fusionfoot ulcersischemiavision transformers

Related Experiment Videos

Last Updated: Jun 25, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

  • Training and validation using the Diabetic Foot Ulcer Challenge (DFUC) 2021 and 2020 datasets.

    • Main Results:

    • Achieved state-of-the-art performance on DFUC 2021 with 98.7% accuracy, 97.3% precision, 97.4% recall, and 97.3% F1-score.
    • Demonstrated robust generalization on DFUC 2020 with 96.9% accuracy, outperforming baseline models.
    • The model successfully classified DFUs into ischemia and infection categories with high performance and interpretability.

    Conclusions:

    • The proposed model offers a significant advancement in automated, explainable DFU classification.
    • Findings support improved clinical decision-making, patient outcomes, and scalable management of diabetic foot complications.