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Diabetic Foot Ulcer

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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An explainable deep learning model for diabetic foot ulcer classification using swin transformer and efficient

R Karthik¹, Armaano Ajay², Anshika Jhalani³

¹Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India.

Scientific Reports

|February 3, 2025

Summary

A novel deep learning model combining Swin transformer and EMADN networks accurately classifies Diabetic Foot Ulcers (DFU). This dual-track approach improves diagnosis, potentially reducing amputations and healthcare costs.

Keywords:

CNN Deep learning Diabetic foot ulcer Shuffle attention Swin transformer

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Diabetic Foot Ulcer (DFU) is a severe diabetes complication, leading to lower limb amputation and significant healthcare burdens.
Manual DFU diagnosis is challenging due to diverse visual characteristics, often resulting in missed diagnoses.

Purpose of the Study:

To develop an automated, efficient deep learning model for accurate Diabetic Foot Ulcer classification.
To introduce a novel dual-track feature fusion architecture for enhanced DFU detection.

Main Methods:

A novel dual-track model integrating Swin transformer for long-range dependencies and Efficient Multi-Scale Attention-Driven Network (EMADN) for local features.
Feature maps from both tracks are concatenated and refined using shuffle attention.
Grad-CAM-based Explainable Artificial Intelligence (XAI) is employed for model interpretability.

Main Results:

The proposed model achieved 78.79% accuracy and 80% macro F1-score on the DFUC-2021 dataset.
Outperformed existing methods and pre-trained Convolutional Neural Network (CNN) architectures.
Demonstrated effective feature extraction and refinement through the dual-track architecture.

Conclusions:

The novel dual-track deep learning model offers a promising solution for automated and accurate Diabetic Foot Ulcer classification.
This approach has the potential to improve early diagnosis, facilitate timely treatment, and reduce the incidence of lower limb amputations in diabetic patients.