Dense-ShuffleGCANet: An Attention-Driven Deep Learning Approach for Diabetic Foot Ulcer Classification Using Refined Spatio-Dimensional Features
Diabetic foot ulcers (DFU) are a common and serious complication of diabetes, often leading to severe health implications like limb amputation if left untreated. Timely intervention and treatment are crucial in mitigating the impact of DFU on patients’ well-being. However, manual identifi...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10819349/ |
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| author | Armaano Ajay Akshaj Singh Bisht R. Karthik |
| author_facet | Armaano Ajay Akshaj Singh Bisht R. Karthik |
| author_sort | Armaano Ajay |
| collection | DOAJ |
| description | Diabetic foot ulcers (DFU) are a common and serious complication of diabetes, often leading to severe health implications like limb amputation if left untreated. Timely intervention and treatment are crucial in mitigating the impact of DFU on patients’ well-being. However, manual identification of DFUs remains a challenge due to their heterogeneous visual characteristics, leading to several undiagnosed cases and avoidable complications. Deep learning provides an efficient solution to the challenge by automating the process of DFU detection, thereby alleviating the burden on the healthcare industry. This research introduces a novel model, Dense-ShuffleGCANet, for DFU detection by leveraging DenseNet-169, Channel-Centric Depth-wise Group Shuffle (CCDGS) block, and triplet attention. The architecture of DenseNet-169 is modified to integrate the CCDGS block and triplet attention, enabling the model to efficiently capture long-range dependencies and utilise cross-channel features. These improvements enable the effective extraction of both channel-wise and spatial features. The proposed Dense-ShuffleGCANet model achieved an accuracy of 86.09% and an F1-score of 85.77% on the DFUC2021 dataset, outperforming state-of-the-art architectures. |
| format | Article |
| id | doaj-art-aac6417fec2f4bd79561e71da1ed1f20 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-aac6417fec2f4bd79561e71da1ed1f202025-01-10T00:01:28ZengIEEEIEEE Access2169-35362025-01-01135507552110.1109/ACCESS.2024.352454910819349Dense-ShuffleGCANet: An Attention-Driven Deep Learning Approach for Diabetic Foot Ulcer Classification Using Refined Spatio-Dimensional FeaturesArmaano Ajay0https://orcid.org/0009-0009-2752-1352Akshaj Singh Bisht1https://orcid.org/0009-0003-9118-465XR. Karthik2https://orcid.org/0000-0002-5250-4337School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Chennai, IndiaSchool of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Chennai, IndiaCentre for Cyber-Physical Systems (CCPS), Vellore Institute of Technology, Chennai, IndiaDiabetic foot ulcers (DFU) are a common and serious complication of diabetes, often leading to severe health implications like limb amputation if left untreated. Timely intervention and treatment are crucial in mitigating the impact of DFU on patients’ well-being. However, manual identification of DFUs remains a challenge due to their heterogeneous visual characteristics, leading to several undiagnosed cases and avoidable complications. Deep learning provides an efficient solution to the challenge by automating the process of DFU detection, thereby alleviating the burden on the healthcare industry. This research introduces a novel model, Dense-ShuffleGCANet, for DFU detection by leveraging DenseNet-169, Channel-Centric Depth-wise Group Shuffle (CCDGS) block, and triplet attention. The architecture of DenseNet-169 is modified to integrate the CCDGS block and triplet attention, enabling the model to efficiently capture long-range dependencies and utilise cross-channel features. These improvements enable the effective extraction of both channel-wise and spatial features. The proposed Dense-ShuffleGCANet model achieved an accuracy of 86.09% and an F1-score of 85.77% on the DFUC2021 dataset, outperforming state-of-the-art architectures.https://ieeexplore.ieee.org/document/10819349/CNNdeep learningDenseNetdiabetic foot ulcertriplet attention |
| spellingShingle | Armaano Ajay Akshaj Singh Bisht R. Karthik Dense-ShuffleGCANet: An Attention-Driven Deep Learning Approach for Diabetic Foot Ulcer Classification Using Refined Spatio-Dimensional Features IEEE Access CNN deep learning DenseNet diabetic foot ulcer triplet attention |
| title | Dense-ShuffleGCANet: An Attention-Driven Deep Learning Approach for Diabetic Foot Ulcer Classification Using Refined Spatio-Dimensional Features |
| title_full | Dense-ShuffleGCANet: An Attention-Driven Deep Learning Approach for Diabetic Foot Ulcer Classification Using Refined Spatio-Dimensional Features |
| title_fullStr | Dense-ShuffleGCANet: An Attention-Driven Deep Learning Approach for Diabetic Foot Ulcer Classification Using Refined Spatio-Dimensional Features |
| title_full_unstemmed | Dense-ShuffleGCANet: An Attention-Driven Deep Learning Approach for Diabetic Foot Ulcer Classification Using Refined Spatio-Dimensional Features |
| title_short | Dense-ShuffleGCANet: An Attention-Driven Deep Learning Approach for Diabetic Foot Ulcer Classification Using Refined Spatio-Dimensional Features |
| title_sort | dense shufflegcanet an attention driven deep learning approach for diabetic foot ulcer classification using refined spatio dimensional features |
| topic | CNN deep learning DenseNet diabetic foot ulcer triplet attention |
| url | https://ieeexplore.ieee.org/document/10819349/ |
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