{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T04:39:51Z","timestamp":1789274391604,"version":"build-2803163510"},"reference-count":25,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2021,2,28]],"date-time":"2021-02-28T00:00:00Z","timestamp":1614470400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Dermatologic Therapy"],"published-print":{"date-parts":[[2021,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>During the COVID\u201019 pandemic, dermatologists reported an array of different cutaneous manifestations of the disease. It is challenging to discriminate COVID\u201019\u2010related cutaneous manifestations from other closely resembling skin lesions. The aim of this study was to generate and evaluate a novel CNN (Convolutional Neural Network) ensemble architecture for detection of COVID\u201019\u2010associated skin lesions from clinical images. An ensemble model of three different CNN\u2010based algorithms was trained with clinical images of skin lesions from confirmed COVID\u201019 positive patients, healthy controls as well as 18 other common skin conditions, which included close mimics of COVID\u201019 skin lesions such as urticaria, varicella, pityriasis rosea, herpes zoster, bullous pemphigoid and psoriasis. The multi\u2010class model demonstrated an overall top\u20101 accuracy of 86.7% for all 20 diseases. The sensitivity and specificity of COVID\u201019\u2010rash detection were found to be 84.2 \u00b1\u20095.1% and 99.5 \u00b1\u20090.2%, respectively. The positive predictive value, NPV and area under curve values for COVID\u201019\u2010rash were 88.0 \u00b1\u20095.6%, 99.4 \u00b1\u20090.2% and 0.97\u2009\u00b1\u20090.25, respectively. The binary classifier had a mean sensitivity, specificity and accuracy of 76.81\u2009\u00b1\u20096.25%, 99.77\u2009\u00b1\u20090.14% and 98.91\u2009\u00b1\u20090.17%, respectively for COVID\u201019 rash. The model was robust in detection of all skin lesions on both white and skin of color, although only a few images of COVID\u201019\u2010associated skin lesions from skin of color were available. To our best knowledge, this is the first machine learning\u2010based study for automated detection of COVID\u201019 based on skin images and may provide a useful decision support tool for physicians to optimize contact\u2010free COVID\u201019 triage, differential diagnosis of skin lesions and patient care.<\/jats:p>","DOI":"10.1111\/dth.14902","type":"journal-article","created":{"date-parts":[[2021,2,19]],"date-time":"2021-02-19T03:04:26Z","timestamp":1613703866000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A convolutional neural network architecture for the recognition of cutaneous manifestations of COVID\u201019"],"prefix":"10.1155","volume":"34","author":[{"given":"Jyoti","family":"Mathur","sequence":"first","affiliation":[{"name":"Nurithm Labs Private Limited  Noida 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