{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T19:41:46Z","timestamp":1791229306627,"version":"4.1.0"},"reference-count":26,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T00:00:00Z","timestamp":1685404800000},"content-version":"vor","delay-in-days":149,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Healthcare Engineering"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p>Skin is the outer cover of our body, which protects vital organs from harm. This important body part is often affected by a series of infections caused by fungus, bacteria, viruses, allergies, and dust. Millions of people suffer from skin diseases. It is one of the common causes of infection in sub\u2010Saharan Africa. Skin disease can also be the cause of stigma and discrimination. Early and accurate diagnosis of skin disease can be vital for effective treatment. Laser and photonics\u2010based technologies are used for the diagnosis of skin disease. These technologies are expensive and not affordable, especially for resource\u2010limited countries like Ethiopia. Hence, image\u2010based methods can be effective in reducing cost and time. There are previous studies on image\u2010based diagnosis for skin disease. However, there are few scientific studies on tinea pedis and tinea corporis. In this study, the convolution neural network (CNN) has been used to classify fungal skin disease. The classification was carried out on the four most common fungal skin diseases: tinea pedis, tinea capitis, tinea corporis, and tinea unguium. The dataset consisted of a total of 407 fungal skin lesions collected from Dr. Gerbi Medium Clinic, Jimma, Ethiopia. Normalization of image size, conversion of RGB to grayscale, and balancing the intensity of the image have been carried out. Images were normalized to three sizes: 120\u2009\u00d7\u2009120, 150\u2009\u00d7\u2009150, and 224\u2009\u00d7\u2009224. Then, augmentation was applied. The developed model classified the four common fungal skin diseases with 93.3% accuracy. Comparisons were made with similar CNN architectures: MobileNetV2 and ResNet 50, and the proposed model was superior to both. This study may be an important addition to the very limited work on the detection of fungal skin disease. It can be used to build an automated image\u2010based screening system for dermatology at an initial stage.<\/jats:p>","DOI":"10.1155\/2023\/6370416","type":"journal-article","created":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T15:14:43Z","timestamp":1685546083000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Fungal Skin Disease Classification Using the Convolutional Neural Network"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6921-0538","authenticated-orcid":false,"given":"Tsedenya Debebe","family":"Nigat","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9872-707X","authenticated-orcid":false,"given":"Tilahun Melak","family":"Sitote","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8602-8770","authenticated-orcid":false,"given":"Berihun Molla","family":"Gedefaw","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijms140612222"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2020.103074"},{"key":"e_1_2_9_3_2","volume-title":"Recognizing Neglected Skin Diseases: WHO Publishes Pictorial Training Guide","author":"Dagne D. 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Skin Diseases 2016 https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK11733\/."},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.j3208"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13671-017-0192-7"},{"key":"e_1_2_9_11_2","first-page":"1","article-title":"Superficial fungal infections. Skin disorders","volume":"4","author":"Kelly B. P.","year":"2012","journal-title":"Pediatrics in Review"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1111\/ijd.14346"},{"key":"e_1_2_9_13_2","doi-asserted-by":"crossref","unstructured":"ZhouY. FengY. andZhangH. Human fungal infection image classification based on convolutional neural network Proceedings of the Chinese Conference on Image and Graphics Technologies July 2019 Singapore Springer.","DOI":"10.1007\/978-981-13-9917-6_1"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.30534\/ijatcse\/2019\/116852019"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2918221"},{"key":"e_1_2_9_16_2","doi-asserted-by":"crossref","unstructured":"KamulegeyaL. H. OkelloM. BwanikaJ. M. MusinguziD. LubegaW. RusokeD. NassiwaF. andB\u00f6rveA. 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Management of covid-19 detection using artificial intelligence in 2020 pandemic Proceedings of the 2021 5th International Conference on Medical and Health Informatics May 2021 Kyoto Japan 32\u201338.","DOI":"10.1145\/3472813.3472820"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.5120\/ijca2016909001"},{"key":"e_1_2_9_25_2","doi-asserted-by":"crossref","unstructured":"NawarA. SabuzN. K. SiddiqueeS. M. T. RabbaniM. BiswasA. A. andMajumderA. 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