{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T16:30:11Z","timestamp":1792168211320,"version":"4.4.0"},"reference-count":31,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T00:00:00Z","timestamp":1681430400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007113","name":"Arak University of Medical Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007113","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Skin cancer is the most common cancer in the United States. Current estimates are that one in five Americans will develop skin cancer in their lifetime. A skin cancer diagnosis is challenging for dermatologists requiring a biopsy from the lesion and histopathological examinations. In this article, we used the HAM10000 dataset to develop a web application that classifies skin cancer lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>This article presents a methodological approach that utilizes dermoscopy images from the HAM10000 dataset, a collection of 10015 dermatoscopic images collected over 20 years from two different sites, to improve the diagnosis of pigmented skin lesions. The study design involves image pre-processing, which includes labelling, resizing, and data augmentation techniques to increase the instances of the dataset. Transfer learning, a machine learning technique, was used to create a model architecture that includes EfficientNET-B1, a variant of the baseline model EfficientNET-B0, with a global average pooling 2D layer and a softmax layer with 7 nodes added on top. The results of the study offer a promising method for dermatologists to improve their diagnosis of pigmented skin lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model performs best in detecting melanocytic nevi lesions with an F1 score of 0.93. The F1 score for Actinic Keratosis, Basal Cell Carcinoma, Benign Keratosis, Dermatofibroma, Melanoma, and Vascular lesions was consecutively 0.63, 0.72, 0.70, 0.54, 0.58, and 0.80.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We classified seven distinct skin lesions in the HAM10000 dataset with an EfficientNet model reaching an accuracy of 84.3%, which provides a promising outlook for further development of more accurate models.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0284437","type":"journal-article","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T13:28:53Z","timestamp":1681478933000},"page":"e0284437","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":37,"title":["Design and validation of a new machine-learning-based diagnostic tool for the differentiation of dermatoscopic skin cancer images"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1753-4263","authenticated-orcid":true,"given":"Amin","family":"Tajerian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohsen","family":"Kazemian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Tajerian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ava","family":"Akhavan Malayeri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2023,4,14]]},"reference":[{"issue":"4","key":"pone.0284437.ref001","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1002\/ijc.33588","article-title":"Cancer statistics for the year 2020: An overview","volume":"149","author":"J Ferlay","year":"2021","journal-title":"International Journal of Cancer"},{"issue":"21","key":"pone.0284437.ref002","first-page":"591","article-title":"Vital signs: melanoma incidence and mortality trends and projections\u2014United States, 1982\u20132030","volume":"64","author":"GP Guy","year":"2015","journal-title":"MMWR Morb Mortal Wkly Rep"},{"issue":"3","key":"pone.0284437.ref003","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1001\/archdermatol.2010.4","article-title":"Prevalence of a history of skin cancer in 2007: results of an incidence-based model","volume":"146","author":"RS Stern","year":"2010","journal-title":"Arch Dermatol"},{"issue":"1","key":"pone.0284437.ref004","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/S1011-1344(01)00198-1","article-title":"The epidemiology of UV induced skin cancer","volume":"63","author":"BK Armstrong","year":"2001","journal-title":"Journal of Photochemistry and Photobiology B: Biology"},{"issue":"1","key":"pone.0284437.ref005","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.clindermatol.2013.05.021","article-title":"Basic histological structure and functions of facial skin","volume":"32","author":"O Arda","year":"2014","journal-title":"Clin Dermatol"},{"key":"pone.0284437.ref006","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.tiv.2018.08.013","article-title":"Human skin explants an in vitro approach for assessing UVB induced damage","volume":"53","author":"C. Khalil","year":"2018","journal-title":"Toxicol In Vitro"},{"key":"pone.0284437.ref007","doi-asserted-by":"crossref","first-page":"666528","DOI":"10.3389\/fpubh.2021.666528","article-title":"Ultraviolet Radiation and Basal Cell Carcinoma: An Environmental Perspective","volume":"9","author":"Y Teng","year":"2021","journal-title":"Front Public Health"},{"issue":"3","key":"pone.0284437.ref008","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s42764-020-00009-8","article-title":"Mechanisms of UV-induced mutations and skin cancer","volume":"1","author":"GP Pfeifer","year":"2020","journal-title":"Genome Instab Dis"},{"issue":"3","key":"pone.0284437.ref009","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.soncn.2013.06.002","article-title":"Skin cancer: an overview of epidemiology and risk factors","volume":"29","author":"R. Gordon","year":"2013","journal-title":"Semin Oncol Nurs"},{"issue":"4","key":"pone.0284437.ref010","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1016\/j.pop.2015.07.006","article-title":"Skin Cancer","volume":"42","author":"MA Linares","year":"2015","journal-title":"Prim Care"},{"issue":"4","key":"pone.0284437.ref011","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.cps.2021.05.001","article-title":"Melanoma Risk Factors and Prevention","volume":"48","author":"WW Dzwierzynski","year":"2021","journal-title":"Clin Plast Surg"},{"issue":"10","key":"pone.0284437.ref012","doi-asserted-by":"crossref","DOI":"10.3390\/ijerph18105479","article-title":"Skin Cancer Detection: A Review Using Deep Learning Techniques","volume":"18","author":"M Dildar","year":"2021","journal-title":"Int J Environ Res Public Health"},{"key":"pone.0284437.ref013","doi-asserted-by":"crossref","unstructured":"Tschandl P. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. In: Vi DIRG, editor. 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