An explainable deep learning model for diabetic foot ulcer classification using swin transformer and efficient multi-scale attention-driven network
Abstract Diabetic Foot Ulcer (DFU) is a severe complication of diabetes mellitus, resulting in significant health and socio-economic challenges for the diagnosed individual. Severe cases of DFU can lead to lower limb amputation in diabetic patients, making their diagnosis a complex and costly proces...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-87519-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823862494986240000 |
|---|---|
| author | R. Karthik Armaano Ajay Anshika Jhalani Kruthik Ballari Suganthi K |
| author_facet | R. Karthik Armaano Ajay Anshika Jhalani Kruthik Ballari Suganthi K |
| author_sort | R. Karthik |
| collection | DOAJ |
| description | Abstract Diabetic Foot Ulcer (DFU) is a severe complication of diabetes mellitus, resulting in significant health and socio-economic challenges for the diagnosed individual. Severe cases of DFU can lead to lower limb amputation in diabetic patients, making their diagnosis a complex and costly process that poses challenges for medical professionals. Manual identification of DFU is particularly difficult due to their diverse visual characteristics, leading to multiple cases going undiagnosed. To address this challenge, Deep Learning (DL) methods offer an efficient and automated approach to facilitate timely treatment and improve patient outcomes. This research proposes a novel feature fusion-based model that incorporates two parallel tracks for efficient feature extraction. The first track utilizes the Swin transformer, which captures long-range dependencies by employing shifted windows and self-attention mechanisms. The second track involves the Efficient Multi-Scale Attention-Driven Network (EMADN), which leverages Light-weight Multi-scale Deformable Shuffle (LMDS) and Global Dilated Attention (GDA) blocks to extract local features efficiently. These blocks dynamically adjust kernel sizes and leverage attention modules, enabling effective feature extraction. To the best of our knowledge, this is the first work reporting the findings of a dual track architecture for DFU classification, leveraging Swin transformer and EMADN networks. The obtained feature maps from both the networks are concatenated and subjected to shuffle attention for feature refinement at a reduced computational cost. The proposed work also incorporates Grad-CAM-based Explainable Artificial Intelligence (XAI) to visualize and interpret the decision making of the network. The proposed model demonstrated better performance on the DFUC-2021 dataset, surpassing existing works and pre-trained CNN architectures with an accuracy of 78.79% and a macro F1-score of 80%. |
| format | Article |
| id | doaj-art-89368a32fa6c42bdba9e283622790664 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-89368a32fa6c42bdba9e2836227906642025-02-09T12:32:04ZengNature PortfolioScientific Reports2045-23222025-02-0115112010.1038/s41598-025-87519-1An explainable deep learning model for diabetic foot ulcer classification using swin transformer and efficient multi-scale attention-driven networkR. Karthik0Armaano Ajay1Anshika Jhalani2Kruthik Ballari3Suganthi K4Centre for Cyber Physical Systems, Vellore Institute of TechnologySchool of Computer Science and Engineering, Vellore Institute of TechnologySchool of Electronics and Engineering, Vellore Institute of TechnologySchool of Computer Science and Engineering, Vellore Institute of TechnologySchool of Electronics and Engineering, Vellore Institute of TechnologyAbstract Diabetic Foot Ulcer (DFU) is a severe complication of diabetes mellitus, resulting in significant health and socio-economic challenges for the diagnosed individual. Severe cases of DFU can lead to lower limb amputation in diabetic patients, making their diagnosis a complex and costly process that poses challenges for medical professionals. Manual identification of DFU is particularly difficult due to their diverse visual characteristics, leading to multiple cases going undiagnosed. To address this challenge, Deep Learning (DL) methods offer an efficient and automated approach to facilitate timely treatment and improve patient outcomes. This research proposes a novel feature fusion-based model that incorporates two parallel tracks for efficient feature extraction. The first track utilizes the Swin transformer, which captures long-range dependencies by employing shifted windows and self-attention mechanisms. The second track involves the Efficient Multi-Scale Attention-Driven Network (EMADN), which leverages Light-weight Multi-scale Deformable Shuffle (LMDS) and Global Dilated Attention (GDA) blocks to extract local features efficiently. These blocks dynamically adjust kernel sizes and leverage attention modules, enabling effective feature extraction. To the best of our knowledge, this is the first work reporting the findings of a dual track architecture for DFU classification, leveraging Swin transformer and EMADN networks. The obtained feature maps from both the networks are concatenated and subjected to shuffle attention for feature refinement at a reduced computational cost. The proposed work also incorporates Grad-CAM-based Explainable Artificial Intelligence (XAI) to visualize and interpret the decision making of the network. The proposed model demonstrated better performance on the DFUC-2021 dataset, surpassing existing works and pre-trained CNN architectures with an accuracy of 78.79% and a macro F1-score of 80%.https://doi.org/10.1038/s41598-025-87519-1Swin transformerDiabetic foot ulcerCNNDeep learningShuffle attention |
| spellingShingle | R. Karthik Armaano Ajay Anshika Jhalani Kruthik Ballari Suganthi K An explainable deep learning model for diabetic foot ulcer classification using swin transformer and efficient multi-scale attention-driven network Scientific Reports Swin transformer Diabetic foot ulcer CNN Deep learning Shuffle attention |
| title | An explainable deep learning model for diabetic foot ulcer classification using swin transformer and efficient multi-scale attention-driven network |
| title_full | An explainable deep learning model for diabetic foot ulcer classification using swin transformer and efficient multi-scale attention-driven network |
| title_fullStr | An explainable deep learning model for diabetic foot ulcer classification using swin transformer and efficient multi-scale attention-driven network |
| title_full_unstemmed | An explainable deep learning model for diabetic foot ulcer classification using swin transformer and efficient multi-scale attention-driven network |
| title_short | An explainable deep learning model for diabetic foot ulcer classification using swin transformer and efficient multi-scale attention-driven network |
| title_sort | explainable deep learning model for diabetic foot ulcer classification using swin transformer and efficient multi scale attention driven network |
| topic | Swin transformer Diabetic foot ulcer CNN Deep learning Shuffle attention |
| url | https://doi.org/10.1038/s41598-025-87519-1 |
| work_keys_str_mv | AT rkarthik anexplainabledeeplearningmodelfordiabeticfootulcerclassificationusingswintransformerandefficientmultiscaleattentiondrivennetwork AT armaanoajay anexplainabledeeplearningmodelfordiabeticfootulcerclassificationusingswintransformerandefficientmultiscaleattentiondrivennetwork AT anshikajhalani anexplainabledeeplearningmodelfordiabeticfootulcerclassificationusingswintransformerandefficientmultiscaleattentiondrivennetwork AT kruthikballari anexplainabledeeplearningmodelfordiabeticfootulcerclassificationusingswintransformerandefficientmultiscaleattentiondrivennetwork AT suganthik anexplainabledeeplearningmodelfordiabeticfootulcerclassificationusingswintransformerandefficientmultiscaleattentiondrivennetwork AT rkarthik explainabledeeplearningmodelfordiabeticfootulcerclassificationusingswintransformerandefficientmultiscaleattentiondrivennetwork AT armaanoajay explainabledeeplearningmodelfordiabeticfootulcerclassificationusingswintransformerandefficientmultiscaleattentiondrivennetwork AT anshikajhalani explainabledeeplearningmodelfordiabeticfootulcerclassificationusingswintransformerandefficientmultiscaleattentiondrivennetwork AT kruthikballari explainabledeeplearningmodelfordiabeticfootulcerclassificationusingswintransformerandefficientmultiscaleattentiondrivennetwork AT suganthik explainabledeeplearningmodelfordiabeticfootulcerclassificationusingswintransformerandefficientmultiscaleattentiondrivennetwork |