A feature explainability-based deep learning technique for diabetic foot ulcer identification
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. Ear...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-90780-z |
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| author | Pramod Singh Rathore Abhishek Kumar Amita Nandal Arvind Dhaka Arpit Kumar Sharma |
| author_facet | Pramod Singh Rathore Abhishek Kumar Amita Nandal Arvind Dhaka Arpit Kumar Sharma |
| author_sort | Pramod Singh Rathore |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2c207a21f6a14e7ba287e2d49f7723a6 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-2c207a21f6a14e7ba287e2d49f7723a62025-08-20T02:16:48ZengNature PortfolioScientific Reports2045-23222025-02-0115112110.1038/s41598-025-90780-zA feature explainability-based deep learning technique for diabetic foot ulcer identificationPramod Singh Rathore0Abhishek Kumar1Amita Nandal2Arvind Dhaka3Arpit Kumar Sharma4Department of Computer and Communication Engineering, Manipal University JaipurDepartment of CSE, Chandigarh UniversityDepartment of IoT and Intelligent Systems, Manipal University JaipurDepartment of Computer and Communication Engineering, Manipal University JaipurDepartment of Computer and Communication Engineering, Manipal University JaipurAbstract 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.https://doi.org/10.1038/s41598-025-90780-zLIMEHeat MapAIDLDiabetic Foot Ulcer |
| spellingShingle | Pramod Singh Rathore Abhishek Kumar Amita Nandal Arvind Dhaka Arpit Kumar Sharma A feature explainability-based deep learning technique for diabetic foot ulcer identification Scientific Reports LIME Heat Map AI DL Diabetic Foot Ulcer |
| title | A feature explainability-based deep learning technique for diabetic foot ulcer identification |
| title_full | A feature explainability-based deep learning technique for diabetic foot ulcer identification |
| title_fullStr | A feature explainability-based deep learning technique for diabetic foot ulcer identification |
| title_full_unstemmed | A feature explainability-based deep learning technique for diabetic foot ulcer identification |
| title_short | A feature explainability-based deep learning technique for diabetic foot ulcer identification |
| title_sort | feature explainability based deep learning technique for diabetic foot ulcer identification |
| topic | LIME Heat Map AI DL Diabetic Foot Ulcer |
| url | https://doi.org/10.1038/s41598-025-90780-z |
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