{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T17:33:47Z","timestamp":1787506427336,"version":"build-2736575974"},"reference-count":35,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BioMed Research International"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>\n                    This research introduces \u201cXMP\u2010Net,\u201d a modified Xception\u2013based deep learning architecture constructed for the categorization of skin conditions, with a particular focus on identifying monkeypox. The study recognizes skin images of four categories: normal, chickenpox, measles, and monkeypox. To enhance interpretability and foster confidence in the model\u2032s predictions, Grad\u2010CAM (gradient\u2010weighted class activation mapping) and LIME (local interpretable model\u2010agnostic explanations) were employed to illustrate the model\u2032s thinking manner. The model demonstrated impressive classification performance, attaining an accuracy of 98.33% for normal skin, 98.25% for monkeypox, 84.21% for measles, and 77.27% for chickenpox. Precision, recall, and\n                    <jats:italic>F<\/jats:italic>\n                    1\u2010score values were also analyzed for each class, with monkeypox achieving a precision of 91.80%, a recall of 98.25%, and an\n                    <jats:italic>F<\/jats:italic>\n                    1\u2010score of 94.92%. The visual explanations generated by Grad\u2010CAM and LIME highlighted critical parts in the input images that affected the model\u2032s likelihoods, offering clinicians valuable insights into the diagnostic process. This research underscores the potential of explainable artificial intelligence (XAI) in augmenting traditional diagnostic methods, particularly for emerging communicable maladies like monkeypox, and provides a foundation for developing reliable, interpretable, and accessible diagnostic tools for resource\u2010constrained settings.\n                  <\/jats:p>","DOI":"10.1155\/bmri\/1113178","type":"journal-article","created":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T08:29:50Z","timestamp":1767860990000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["XMP\u2010Net: An XAI\u2010Based Modified Xception Model for Recognizing Monkeypox and Other Skin Diseases"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0758-2790","authenticated-orcid":false,"family":"Ferdib-Al-Islam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8163-3474","authenticated-orcid":false,"given":"Prithvi","family":"Biswas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6599-9148","authenticated-orcid":false,"given":"Partha Protim","family":"Gharami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5745-4439","authenticated-orcid":false,"given":"Md. 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