{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T01:01:13Z","timestamp":1771030873707,"version":"3.50.1"},"reference-count":50,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T00:00:00Z","timestamp":1722384000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Skin Research and Technology"],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Skin diseases are severe diseases. Identification of these severe diseases depends upon the abstraction of atypical skin regions. The segmentation of these skin diseases is essential to rheumatologists in risk impost and for valuable and vital decision\u2010making. Skin lesion segmentation from images is a crucial step toward achieving this goal\u2014timely exposure of malignancy in psoriasis expressively intensifies the persistence ratio. Defies occur when people presume skin diseases they have without accurately and precisely incepted. However, analyzing malignancy at runtime is a big challenge due to the truncated distinction of the visual similarity between malignance and non\u2010malignance lesions. However, images' different shapes, contrast, and vibrations make skin lesion segmentation challenging. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>This paper introduces a skin lesions segmentation model that integrates two intelligent methodologies: Bayesian inference and edge intelligence. In the segmentation model, we deal with edge intelligence to utilize the texture features for the segmentation of skin lesions. In contrast, Bayesian inference enhances skin lesion segmentation's accuracy and efficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We analyze our work along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions from seminal works and a systematic viewpoint and examine how these dimensions have influenced current trends.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We summarize our work with previously used techniques in a comprehensive table to facilitate comparisons. Our experimental results show that Bayesian\u2010Edge networks can boost the diagnostic performance of skin lesions by up to 87.80% without incurring additional parameters of heavy computation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/srt.13878","type":"journal-article","created":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T03:31:38Z","timestamp":1722396698000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["RETRACTED: Bayesian\u2010Edge system for classification and segmentation of skin lesions in Internet of Medical Things"],"prefix":"10.1111","volume":"30","author":[{"given":"Shahid","family":"Naseem","sequence":"first","affiliation":[{"name":"Department of Information Sciences Division of Science and Technology University of Education  Lahore Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Anwar","sequence":"additional","affiliation":[{"name":"Department of Information Sciences Division of Science and Technology University of Education  Lahore Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4628-4486","authenticated-orcid":false,"given":"Muhammad","family":"Faheem","sequence":"additional","affiliation":[{"name":"School of Technology and Innovations University of Vaasa  Vaasa Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Fayyaz","sequence":"additional","affiliation":[{"name":"Department of Computer Science FAST National University of Computer &amp; Emerging Sciences Chiniot\u2010Faisalabad Campus  Islamabad Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Sheraz Arshad","family":"Malik","sequence":"additional","affiliation":[{"name":"Department of\u00a0Software Engineering Government College University Faisalabad  Faisalabad Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,7,31]]},"reference":[{"issue":"1475","key":"e_1_2_9_2_1","first-page":"1","article-title":"Psoriasis pathhogenesis and treatment","volume":"20","author":"Adriana R","year":"2019","journal-title":"Int J Mol Sci"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105631"},{"key":"e_1_2_9_4_1","volume-title":"Soaring skin cancer cases hit a record high","author":"Wedekind S","year":"2023"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/JTEHM.2023.3282104"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/hsr2.1656"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2022.08.485"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1049\/htl2.12049"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2007.894239"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1049\/htl2.12073"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamadermatol.2021.2007"},{"issue":"4","key":"e_1_2_9_12_1","first-page":"278","article-title":"Diagnosis and management of psoriasis","volume":"63","author":"Whan B","year":"2017","journal-title":"Can Fam Physician"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.5826\/dpc.1001a03"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/jocd.14054"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms20061475"},{"key":"e_1_2_9_16_1","article-title":"AML\u2010Net: Attention\u2010based multi\u2010scale lightweight model for brain tumour segmentation in internet of medical things","author":"Aslam MZ","year":"2024","journal-title":"CAAI Trans Intell Technol"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00105-016-3805-3"},{"key":"e_1_2_9_18_1","first-page":"172","article-title":"Color blindness defects and medical laboratory technologists: unnoticed problems and the care for screening","volume":"48","author":"Daragahr H","year":"2010","journal-title":"Acta Med Iran"},{"issue":"4","key":"e_1_2_9_19_1","first-page":"S39","article-title":"Using dermoscopy to identify melanoma and improve diagnostic discrimination","volume":"35","author":"Holmes GA","year":"2018","journal-title":"Fed Pract"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40265-014-0191-y"},{"key":"e_1_2_9_21_1","volume-title":"WHO Global report on psoriasis","author":"Irmina M","year":"2016"},{"key":"e_1_2_9_22_1","doi-asserted-by":"crossref","unstructured":"HammedN ShabutAM HossainMA.Multi\u2010class skin diseases classification using deep convolutional neural network and support vector machine.IEEE. 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