{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T18:53:05Z","timestamp":1793559185876,"version":"4.5.0"},"reference-count":57,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T00:00:00Z","timestamp":1756771200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Diabetic Foot Ulcer (DFU) is a major complication of diabetes which needs early detection to help in timely treatment for preventing future serious consequences. Due to peripheral neuropathy, high blood glucose levels, and untreated wounds, DFUs can cause the disintegration of the skin and exposing the tissue below it, if not adequately treated. Recently deep learning (DL) has advanced and has shown its ability to automate DFU detection and classification by analysing medical images. The use of DL has been proven to be very useful for healthcare professionals, enabling earlier diagnosis and effective treatment of DFU. However, most of the studies predominantly rely on a single dataset (e.g., DFUC2021 or DFUC2020) without external validation or cross-dataset testing, raising concerns about generalizability and trustworthiness. The aim of this study is to develop a robust, reliable, and transparent DFU detection framework which is not only good performing but also can effectively give attention to the proper region of the images which are crucial for DFU detection. So, to make DFU detection robust, reliable in a single study, we proposed a custom approach, DFU_DIALNet and to enhance transparency and interpret the model decisions in this study, we integrated Grad-CAM and LIME heatmaps to precisely localize ulcer regions. This allows visual verification of the model\u2019s focus and clarifies the decision-making process, thereby increasing the model\u2019s reliability. DFU_DIALNet outperforms all other traditional models with 99.33% accuracy, 99% F1 score, and 100% AUC score, and compared it to other DL models\u2014DenseNet121, MobileNetV2, InceptionV3, EfficientNetB0, ResNet50V2 and VGG16\u2014in the merged dataset of DFUC2021 with our collected 500 images. We have checked our model\u2019s reliability with 2 other popular datasets\u2014-the KDFU and DFUC2020 datasets, where our proposed approach gives the highest accuracy of 95.61% and 99.54%, respectively, compared to other deep learning approaches. Lastly, we have developed a web app using Streamlit to detect DFU efficiently. This study fills the gap between reliable and interpretable systems with a proposed approach to the efficient detection of DFU.<\/jats:p>","DOI":"10.1371\/journal.pone.0330669","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T17:52:02Z","timestamp":1756835522000},"page":"e0330669","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":10,"title":["DFU_DIALNet: Towards reliable and trustworthy diabetic foot ulcer detection with synergistic confluence of Grad-CAM and LIME"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2812-8553","authenticated-orcid":true,"given":"Monirul Islam","family":"Mahmud","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Shihab","family":"Reza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Olid Ali","family":"Akash","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farhana","family":"Elias","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nova","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,9,2]]},"reference":[{"key":"pone.0330669.ref001","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.diabres.2017.03.024","article-title":"IDF Diabetes Atlas: global estimates for the prevalence of diabetes for 2015 and 2040","volume":"128","author":"K Ogurtsova","year":"2017","journal-title":"Diabetes Res Clin Pract"},{"issue":"5","key":"pone.0330669.ref002","doi-asserted-by":"crossref","first-page":"855","DOI":"10.2337\/diacare.21.5.855","article-title":"Validation of a diabetic wound classification system. 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Faster R-CNN approach for diabetic foot ulcer detection. In: Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2021. p. 677\u201384. https:\/\/doi.org\/10.5220\/0010255506770684","DOI":"10.5220\/0010255506770684"},{"key":"pone.0330669.ref036","doi-asserted-by":"crossref","unstructured":"Xie C. FCFNet: a network fusing color features, focal loss for diabetic foot ulcer image classification. In: Tanveer M, Agarwal S, Ozawa S, Ekbal A, Jatowt A and editors. Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1793. 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A deep learning model-based neural network framework for diabetic foot ulcer classification. 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