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. 2025 Feb 25;15(1):6758.
doi: 10.1038/s41598-025-90780-z.

A feature explainability-based deep learning technique for diabetic foot ulcer identification

Pramod Singh Rathore  1 , Abhishek Kumar  2 , Amita Nandal  3 , Arvind Dhaka  1 , Arpit Kumar Sharma  1
Affiliations

A feature explainability-based deep learning technique for diabetic foot ulcer identification

Pramod Singh Rathore et al. Sci Rep. .

Abstract

Diabetic foot ulcers (DFUs) are a common and serious complication of diabetes, presenting as open sores or wounds on the sole. They result from impaired blood circulation and neuropathy associated with diabetes, increasing the risk of severe infections and even amputations if untreated. Early detection, effective wound care, and diabetes management are crucial to prevent and treat DFUs. Artificial intelligence (AI), particularly through deep learning, has revolutionized DFU diagnosis and treatment. This work introduces the DFU_XAI framework to enhance the interpretability of deep learning models for DFU labeling and localization, ensuring clinical relevance. The framework evaluates six advanced models-Xception, DenseNet121, ResNet50, InceptionV3, MobileNetV2, and Siamese Neural Network (SNN)-using interpretability techniques like SHAP, LIME, and Grad-CAM. Among these, the SNN model excelled with 98.76% accuracy, 99.3% precision, 97.7% recall, 98.5% F1-score, and 98.6% AUC. Grad-CAM heat maps effectively identified ulcer locations, aiding clinicians with precise and visually interpretable insights. The DFU_XAI framework integrates explainability into AI-driven healthcare, enhancing trust and usability in clinical settings. This approach addresses challenges of transparency in AI for DFU management, offering reliable and efficient solutions to this critical healthcare issue. Traditional DFU methods are labor-intensive and costly, highlighting the transformative potential of AI-driven systems.

Keywords: AI; DL; Diabetic Foot Ulcer; Heat Map; LIME.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests. Ethical approval: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
DFU_XAI Framework.
Algorithm
Algorithm
Planned DFX_XAI Outline for DFU Finding
Fig. 2
Fig. 2
Ulcer Skin normal and abnormal Images [Available at: google.com/search/images].
Fig. 3
Fig. 3
Framework for number recognition with CNN’s.
Fig. 4
Fig. 4
SNN Architecture.
Fig. 5
Fig. 5
Results of the training simulation using the DFU dataset for the proposed DFU_XAI framework (a) model trained without overfitting, and (b) trajectory of the loss function.
Fig. 6
Fig. 6
(a) Predicted table, and (b) receiver operating characteristic.
Fig. 7
Fig. 7
Heat maps for the diabetic foot ulcers.
Fig. 8
Fig. 8
DFU_XUI framework compared with Ultra Modern Network.
Fig. 9
Fig. 9
Three gradient-based approach testing results.
Fig. 10
Fig. 10
CNN and ROC curves comparison.
See this image and copyright information in PMC

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