{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:47:22Z","timestamp":1786978042577,"version":"build-2736575974"},"reference-count":43,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T00:00:00Z","timestamp":1658707200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100004054","name":"King Abdulaziz University","doi-asserted-by":"publisher","award":["IFFPP\u2010116\u201022"],"award-info":[{"award-number":["IFFPP\u2010116\u201022"]}],"id":[{"id":"10.13039\/501100004054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Microscopy Res &amp;amp; Technique"],"published-print":{"date-parts":[[2022,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:label\/>\n                    <jats:p>Skin cancer occurrences increase exponentially worldwide due to the lack of awareness of significant populations and skin specialists. Medical imaging can help with early detection and more accurate diagnosis of skin cancer. The physicians usually follow the manual diagnosis method in their clinics but nonprofessional dermatologists sometimes affect the accuracy of the results. Thus, the automated system is required to assist physicians in diagnosing skin cancer at early stage precisely to decrease the mortality rate. This article presents an automatic skin lesions detection through a microscopic hybrid feature set and machine learning\u2010based classification. The employment of deep features through AlexNet architecture with local optimal\u2010oriented pattern can accurately predict skin lesions. The proposed model is tested on two open\u2010access datasets PAD\u2010UFES\u201020 and MED\u2010NODE comprising melanoma and nevus images. Experimental results on both datasets exhibit the efficacy of hybrid features with the help of machine learning. Finally, the proposed model achieved 94.7% accuracy using an ensemble classifier.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Research highlights<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>The deep features accurately predicted skin lesions through AlexNet architecture with local optimal\u2010oriented pattern.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Proposed model is tested on two datasets PAD\u2010UFES\u201020, MED\u2010NODE comprising melanoma, nevus images and exhibited high accuracy.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jemt.24211","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T08:00:03Z","timestamp":1658736003000},"page":"3600-3607","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Automatic skin lesions detection from images through microscopic hybrid features set and machine learning classifiers"],"prefix":"10.1002","volume":"85","author":[{"given":"Jaber","family":"Alyami","sequence":"first","affiliation":[{"name":"Department of Diagnostic Radiology King Abdulaziz University  Jeddah Saudi Arabia"},{"name":"Animal House Unit, King Fahd Medical Research Center King Abdulaziz University  Jeddah Saudi Arabia"},{"name":"Smart Medical Imaging Research Group King Abdulaziz University  Jeddah Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3817-2655","authenticated-orcid":false,"given":"Amjad","family":"Rehman","sequence":"additional","affiliation":[{"name":"Artificial Intelligence &amp; Data Analytics Lab, CCIS, Prince Sultan University  Riyadh Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0078-7849","authenticated-orcid":false,"given":"Tariq","family":"Sadad","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Software Engineering International Islamic University  Islamabad Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maryam","family":"Alruwaythi","sequence":"additional","affiliation":[{"name":"Artificial Intelligence &amp; Data Analytics Lab, CCIS, Prince Sultan University  Riyadh Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tanzila","family":"Saba","sequence":"additional","affiliation":[{"name":"Artificial Intelligence &amp; Data Analytics Lab, CCIS, Prince Sultan University  Riyadh Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeed Ali","family":"Bahaj","sequence":"additional","affiliation":[{"name":"MIS Department, College of Business Administration Prince Sattam Bin Abdulaziz University  Al\u2010Kharj Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2020.104090"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.23301"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2021.100036"},{"key":"e_1_2_9_5_1","volume-title":"Facts & Figures 2019","author":"American Cancer Society","year":"2019"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04650-7"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8060672"},{"key":"e_1_2_9_8_1","volume-title":"Proceedings of the Computer Visual Pattern Recognition","author":"Chakraborti T.","year":"2017"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24888-2_15"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.04.034"},{"issue":"8","key":"e_1_2_9_11_1","first-page":"3451","article-title":"An evolution based hybrid approach for heart diseases classification and associated risk factors identification","volume":"28","author":"Iftikhar S.","year":"2017","journal-title":"Biomedical Research"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.22867"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13721-019-0209-1"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2021.014677"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113742"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.23220"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCISci.2019.8716400"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2020.106960"},{"key":"e_1_2_9_19_1","article-title":"Skin lesion segmentation and classification: A unified framework of deep neural network features fusion and selection","author":"Khan M. 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