{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T07:55:38Z","timestamp":1787730938168,"version":"build-2784847793"},"reference-count":37,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"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>Mpox lesions can resemble other dermatological conditions, motivating image-based screening, yet published studies remain difficult to compare owing to differences in dataset construction, augmentation policy, and evaluation design. This study provides a leakage-aware benchmark for binary mpox classification using a unified dataset assembled from MSLD v1.0 and v2.0. Seven pretrained backbones and a weighted ensemble were compared under group-stratified five-fold cross-validation with original-only test evaluation, validation-based threshold selection, and temperature scaling. The weighted ensemble achieved mean accuracy 0.8729, F1-score 0.8334, and AUC 0.9388; ConvNeXt-Tiny was the strongest single model (F1 0.8159, AUC 0.9284). These grouped original-only results are intentionally conservative relative to augmentation-heavy or single-split designs and should be interpreted as deflated but more trustworthy reference values. Post hoc calibration analysis, content-level near-duplicate auditing, and a test-time augmentation ablation are provided to substantiate the methodological claims. The contribution is methodological: a transparent benchmark emphasizing reproducible dataset curation, grouped evaluation, and calibrated comparison, while highlighting the limitations of current public skin-image data. Accordingly, these results should be interpreted as a reproducible reference benchmark rather than a clinically validated diagnostic tool, and external clinical validation remains necessary before deployment.<\/jats:p>","DOI":"10.1371\/journal.pone.0352161","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T18:01:36Z","timestamp":1783706496000},"page":"e0352161","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["AI-driven diagnosis of mpox using deep learning models"],"prefix":"10.1371","volume":"21","author":[{"given":"Bassam W.","family":"Aboshosha","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shafiq Ul","family":"Rehman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lamees 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In: Proceedings of the 38th International Conference on Machine Learning (ICML), 2021. 10096\u2013106. https:\/\/doi.org\/10.48550\/arXiv.2104.00298"},{"key":"pone.0352161.ref026","doi-asserted-by":"crossref","unstructured":"Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S. A ConvNet for the 2020s. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 11966\u201376. https:\/\/doi.org\/10.1109\/cvpr52688.2022.01167","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"pone.0352161.ref027","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In: 2021. https:\/\/doi.org\/10.48550\/arXiv.2010.11929"},{"key":"pone.0352161.ref028","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 770\u20138. https:\/\/doi.org\/10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"pone.0352161.ref029","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely Connected Convolutional Networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 2261\u20139. https:\/\/doi.org\/10.1109\/cvpr.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"pone.0352161.ref030","unstructured":"Tan M, Le QV. In: Proceedings of the 36th International Conference on Machine Learning (ICML), 2019. 6105\u201314. https:\/\/doi.org\/10.48550\/arXiv.1905.11946"},{"key":"pone.0352161.ref031","doi-asserted-by":"crossref","unstructured":"Howard A, Sandler M, Chen B, Wang W, Chen L-C, Tan M, et al. Searching for MobileNetV3. 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