{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T09:30:09Z","timestamp":1777714209447,"version":"3.51.4"},"reference-count":27,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,8,29]],"date-time":"2021-08-29T00:00:00Z","timestamp":1630195200000},"content-version":"vor","delay-in-days":240,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Skin cancer is one of the most common types of cancers that is sometimes difficult for doctors and experts to diagnose. The noninvasive dermatoscopic method is a popular method for observing and diagnosing skin cancer. Because this method is based on ocular inference, the skin cancer diagnosis by the dermatologists is difficult, especially in the early stages of the disease. Artificial intelligence is a proper complementary tool that can be used alongside the experts to increase the accuracy of the diagnosis. In the present study, a new computer\u2010aided method has been introduced for the diagnosis of the skin cancer. The method is designed based on combination of deep learning and a newly introduced metaheuristic algorithm, namely, Wildebeest Herd Optimization (WHO) Algorithm. The method uses an Inception convolutional neural network for the initial features\u2019 extraction. Afterward, the WHO algorithm has been employed for selecting the useful features to decrease the analysis time complexity. The method is then performed to an ISIC\u20102008 skin cancer dataset. Final results of the feature selection based on the proposed WHO are compared with three other algorithms, and the results have indicated good results for the system. Finally, the total diagnosis system has been compared with five other methods to indicate its effectiveness against the studied methods. Final results showed that the proposed method has the best results than the comparative methods.<\/jats:p>","DOI":"10.1155\/2021\/7567870","type":"journal-article","created":{"date-parts":[[2021,8,29]],"date-time":"2021-08-29T17:50:06Z","timestamp":1630259406000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["An Improved CNN Architecture to Diagnose Skin Cancer in Dermoscopic Images Based on Wildebeest Herd Optimization Algorithm"],"prefix":"10.1155","volume":"2021","author":[{"given":"Biying","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4984-7224","authenticated-orcid":false,"given":"Behdad","family":"Arandian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,8,29]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.12928\/telkomnika.v17i2.9547"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2926837"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-020-01550-2"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.18280\/ts.370204"},{"key":"e_1_2_9_5_2","doi-asserted-by":"crossref","unstructured":"AinQ. 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A distributed Newton method for network optimization Proceedings of the 48th IEEE Conference on Decision and Control (CDC) Held Jointly with 2009 28th Chinese Control Conference 2009 Shanghai China IEEE 2736\u20132741.","DOI":"10.1109\/CDC.2009.5400289"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2019.103300"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40313-016-0242-6"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12065-019-00199-5"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.3233\/jifs-190495"},{"key":"e_1_2_9_21_2","unstructured":"ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection 2008 https:\/\/challenge2018.isic-archive.com."},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113338"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/tevc.2008.919004"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-16339-6_5"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-018-5714-1"},{"key":"e_1_2_9_26_2","doi-asserted-by":"crossref","unstructured":"LinsanganN. 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Geometric analysis of skin lesion for skin cancer using image processing Proceedings of the 2018 IEEE 10th International Conference on Humanoid Nanotechnology Information Technology Communication and Control Environment and Management (HNICEM) 2018 Baguio City Philippines 1\u20135.","DOI":"10.1109\/HNICEM.2018.8666296"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.31033\/ijemr.9.2.13"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/7567870.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/7567870.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/7567870","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T12:10:34Z","timestamp":1722946234000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/7567870"}},"subtitle":[],"editor":[{"given":"V.","family":"Rajinikanth","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":27,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/7567870"],"URL":"https:\/\/doi.org\/10.1155\/2021\/7567870","archive":["Portico"],"relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-07-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-08-16","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-08-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"7567870"}}