{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T10:52:12Z","timestamp":1776077532580,"version":"3.50.1"},"reference-count":36,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T00:00:00Z","timestamp":1713225600000},"content-version":"vor","delay-in-days":15,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Skin Research and Technology"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background\/purpose<\/jats:title>\n                    <jats:p>Because atopic dermatitis (AD) is a chronic inflammatory skin condition that causes structural changes, there is a growing need for noninvasive research methods to evaluate this condition. Hyperspectral imaging (HSI) captures skin structure features by exploiting light wavelength variations in penetration depth. In this study, parameter\u2010based transfer learning was deployed to classify the severity of AD using HSI. Therefore, we aimed to obtain an optimal combination of classification results from the four models after constructing different source\u2010 and target\u2010domain datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We designated psoriasis, skin cancer, eczema, and AD datasets as the source datasets, and the set of images acquired via hyperspectral camera as the target dataset for wavelength\u2010specific AD classification. We compared the severity classification performances of 96 combinations of sources, models, and targets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The highest classification performance of 83% was achieved when ResNet50 was trained on the augmented psoriasis dataset as the source, with the resulting parameters used to train the model on the target Near\u2010infrared radiation (NIR) dataset. The second highest classification accuracy of 81% was achieved when ResNet50 was trained on the unaugmented psoriasis dataset as the source, with the resulting parameters used to train the model on the target R dataset. ResNet50 demonstrated potential as a generalized model for both the source and target data, also confirming that the psoriasis dataset is an effective training resource.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The present study not only demonstrates the feasibility of the severity classification of AD based on hyperspectral images, but also showcases combinations and research scalability for domain exploration.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/srt.13704","type":"journal-article","created":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T00:50:06Z","timestamp":1713315006000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["RETRACTED: Parameter\u2010based transfer learning for severity classification of atopic dermatitis using hyperspectral imaging"],"prefix":"10.1111","volume":"30","author":[{"given":"Eun Bin","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Software Convergence, Graduate School Soonchunhyang University  Asan City Chungcheongnam\u2010do South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8667-2814","authenticated-orcid":false,"given":"Yoo Sang","family":"Baek","sequence":"additional","affiliation":[{"name":"Department of Dermatology, College of Medicine Korea University  Seoul South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Onseok","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Software Convergence, Graduate School Soonchunhyang University  Asan City Chungcheongnam\u2010do South Korea"},{"name":"Department of Medical IT Engineering, College of Software Convergence Soonchunhyang University Asan City Chungcheongnam\u2010do South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,4,16]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.09.116"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.4992835"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-016-0043-6"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.3004555"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/su15075930"},{"key":"e_1_2_9_9_1","article-title":"Transfusion: understanding transfer learning for medical imaging","volume":"32","author":"Raghu M","year":"2019","journal-title":"Adv Neural Inf Process 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