{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T14:21:52Z","timestamp":1780928512510,"version":"3.54.1"},"reference-count":47,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T00:00:00Z","timestamp":1626307200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Healthcare Engineering"],"published-print":{"date-parts":[[2021,7,15]]},"abstract":"<jats:p>Dermatological problems are the most widely spread skin diseases amongst human beings. They can be infectious, chronic, and sometimes may also lead to serious health problems such as skin cancer. Generally, rural area clinics lack trained dermatologists and mostly rely on the analysis of remotely accessible experts through mobile-based networks for sharing the images and other related information. Under such circumstances, poor image quality introduced due to the capturing device results in misleading diagnosis. Here, a genetic-algorithm- (GA-) based approach used as an image enhancement technique has been explored to improve the low quality of the dermatological images received from the rural clinic. The diagnosis is performed on the enhanced images using convolutional neural network (CNN) classifier for the identification of the diseases. The scope of this paper is limited to only motion blurred images, which is the most prevalent problem in capturing of the images, specifically when any of the two (device or the object) may move unpredictably. Seven types of skin diseases, namely, melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, vascular lesion, and squamous cell carcinoma, have been investigated using ResNet-152 giving an overall accuracy of 87.40% for the blurred images. Use of GA-enhanced images increased the accuracy to 95.85%. The results were further analyzed using a confusion matrix and t-test-based statistical investigations. The advantage of the proposed technique is that it reduces the analysis time and errors due to manual diagnosis. Furthermore, speedy and reliable diagnosis at the earliest stage reduces the risk of developing more severe skin problems.<\/jats:p>","DOI":"10.1155\/2021\/8113403","type":"journal-article","created":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T17:20:07Z","timestamp":1626456007000},"page":"1-13","source":"Crossref","is-referenced-by-count":7,"title":["An Evolutionary Approach for the Enhancement of Dermatological Images and Their Classification Using Deep Learning Models"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5479-3128","authenticated-orcid":true,"family":"Chaahat","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra 182301, India"},{"name":"MIET, Jammu 181122, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4616-2227","authenticated-orcid":true,"given":"Naveen","family":"Kumar Gondhi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra 182301, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4633-4965","authenticated-orcid":true,"given":"Parveen","family":"Kumar Lehana","sequence":"additional","affiliation":[{"name":"Department of Electronics, University of Jammu, Jammu 180006, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2929857"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.15585\/mmwr.mm6439a11"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21590"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1590\/abd1806-4841.20132233"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21442"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21442"},{"key":"7","volume-title":"Cancer Facts & Figures 2019","author":"American Cancer Society","year":"2019"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21492"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1049\/trit.2019.0028"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1049\/trit.2019.0051"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1049\/trit.2018.1006"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-02149-6"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1142\/s0218001421510046"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-02669-6"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.37965\/jait.2021.0017"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.37965\/jait.2020.0051"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.37965\/jait.2020.0037"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.23429"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.2174\/1573405615666191223122401"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.11.034"},{"issue":"9","key":"22","doi-asserted-by":"crossref","first-page":"1898","DOI":"10.1109\/TBME.2018.2856101","article-title":"Guest editorial special issue on deep learning in medical imaging","volume":"65","year":"2018","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"23","article-title":"Convolutional neural network committees for melanoma classification with classical and expert knowledge based image transforms data augmentation","author":"C. 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