{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T15:59:27Z","timestamp":1791647967987,"version":"4.3.4"},"reference-count":46,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T00:00:00Z","timestamp":1732752000000},"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":["Health Care Science"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Skin cancer poses a significant global health threat, with early detection being essential for successful treatment. While deep learning algorithms have greatly enhanced the categorization of skin lesions, the black\u2010box nature of many models limits interpretability, posing challenges for dermatologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To address these limitations, SkinSage XAI utilizes advanced explainable artificial intelligence (XAI) techniques for skin lesion categorization. A data set of around 50,000 images from the Customized HAM10000, selected for diversity, serves as the foundation. The Inception v3 model is used for classification, supported by gradient\u2010weighted class activation mapping and local interpretable model\u2010agnostic explanations algorithms, which provide clear visual explanations for model outputs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      SkinSage XAI demonstrated high performance, accurately categorizing seven types of skin lesions\u2014dermatofibroma, benign keratosis, melanocytic nevus, vascular lesion, actinic keratosis, basal cell carcinoma, and melanoma. It achieved an accuracy of 96%, with precision at 96.42%, recall at 96.28%,\n                      <jats:sc>\n                        <jats:italic>F<\/jats:italic>\n                        <jats:sub>1<\/jats:sub>\n                      <\/jats:sc>\n                      score at 96.14%, and an area under the curve of 99.83%.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>SkinSage XAI represents a significant advancement in dermatology and artificial intelligence by bridging gaps in accuracy and explainability. The system provides transparent, accurate diagnoses, improving decision\u2010making for dermatologists and potentially enhancing patient outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hcs2.121","type":"journal-article","created":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T01:50:34Z","timestamp":1732845034000},"page":"438-455","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["SkinSage XAI: An explainable deep learning solution for skin lesion diagnosis"],"prefix":"10.1002","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5213-9993","authenticated-orcid":false,"given":"Geetika","family":"Munjal","sequence":"first","affiliation":[{"name":"Amity School of Engineering and Technology Amity University Noida Noida Uttar Pradesh India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paarth","family":"Bhardwaj","sequence":"additional","affiliation":[{"name":"Amity School of Engineering and Technology Amity University Noida Noida Uttar Pradesh India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vaibhav","family":"Bhargava","sequence":"additional","affiliation":[{"name":"Amity School of Engineering and Technology Amity University Noida Noida Uttar Pradesh India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shivendra","family":"Singh","sequence":"additional","affiliation":[{"name":"Amity School of Engineering and Technology Amity University Noida Noida Uttar Pradesh India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nimish","family":"Nagpal","sequence":"additional","affiliation":[{"name":"Amity School of Engineering and Technology Amity University Noida Noida Uttar Pradesh India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,11,28]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.161"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0316-z"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbx044"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.03.090"},{"key":"e_1_2_12_6_1","volume-title":"Radiation: ultraviolet (UV) radiation and skin cancer","year":"2017"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2009.2017529"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2019.8857334"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13071314"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/SIU49456.2020.9302125"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3003890"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CIBEC.2018.8641815"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCA49541.2020.9250855"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.18178\/ijmlc.2018.8.1.664"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.24425\/ijet.2019.129818"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIPTEKNO47231.2019.8972045"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2018.06.006"},{"key":"e_1_2_12_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30642-7_30"},{"key":"e_1_2_12_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-80432-9_1"},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"e_1_2_12_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI45749.2020.9098645"},{"key":"e_1_2_12_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-02628-8_16"},{"key":"e_1_2_12_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2972964"},{"key":"e_1_2_12_24_1","unstructured":"RidellP SpettH.Training set size for skin cancer classification using Google's Inception v3. 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