{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T16:38:19Z","timestamp":1791995899348,"version":"4.4.0"},"reference-count":36,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T00:00:00Z","timestamp":1698105600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T00:00:00Z","timestamp":1698105600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100014538","name":"Lembaga Pengelola Dana Pendidikan","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014538","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Skin Research and Technology"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Pigmented skin lesions (PSLs) pose medical and esthetic challenges for those affected. PSLs can cause skin cancers, particularly melanoma, which can be life\u2010threatening. Detecting and treating melanoma early can reduce mortality rates. Dermoscopic imaging offers a noninvasive and cost\u2010effective technique for examining PSLs. However, the lack of standardized colors, image capture settings, and artifacts makes accurate analysis challenging. Computer\u2010aided diagnosis (CAD) using deep learning models, such as convolutional neural networks (CNNs), has shown promise by automatically extracting features from medical images. Nevertheless, enhancing the CNN models' performance remains challenging, notably concerning sensitivity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>In this study, we aim to enhance the classification performance of selected pretrained CNNs. We use the 2019 ISIC dataset, which presents eight disease classes. To achieve this goal, two methods are applied: resolution of the dataset imbalance challenge through augmentation and optimization of the training hyperparameters via Bayesian tuning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The performance improvement was observed for all tested pretrained CNNs. The Inception\u2010V3 model achieved the best performance compared to similar results, with an accuracy of 96.40% and an AUC of 0.98.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>According to the study, classification performance was significantly enhanced by augmentation and Bayesian hyperparameter tuning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/srt.13505","type":"journal-article","created":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T02:27:28Z","timestamp":1698200848000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Boosting the performance of pretrained CNN architecture on dermoscopic pigmented skin lesion classification"],"prefix":"10.1111","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3794-8439","authenticated-orcid":false,"given":"Erwin Setyo","family":"Nugroho","sequence":"first","affiliation":[{"name":"Engineering Faculty, Department of Electrical Engineering and Information Technology Universitas Gadjah Mada Yogyakarta Indonesia"},{"name":"Department of Informatics Politeknik Caltex Riau Riau Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0006-1458","authenticated-orcid":false,"given":"Igi","family":"Ardiyanto","sequence":"additional","affiliation":[{"name":"Engineering Faculty, Department of Electrical Engineering and Information Technology Universitas Gadjah Mada Yogyakarta Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7749-8044","authenticated-orcid":false,"given":"Hanung Adi","family":"Nugroho","sequence":"additional","affiliation":[{"name":"Engineering Faculty, Department of Electrical Engineering and Information Technology Universitas Gadjah Mada Yogyakarta Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,10,24]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"The American Cancer Society.Cancer facts and figures 2023. Details on howpublished.2023. Accessed June 10 2023.https:\/\/www.cancer.org\/content\/dam\/cancer\u2010org\/research\/cancer\u2010facts\u2010and\u2010statistics\/annual\u2010cancer\u2010facts\u2010and\u2010figures\/2023\/2023\u2010cancer\u2010facts\u2010and\u2010figures.pdf"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2018.04.028"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1201\/b19107"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1440-0960.2010.00708.x"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2019.04.001"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/srt.12911"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2859898"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2839647"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32692-0_12"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2940794"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2921815"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/srt.13150"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.23301"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2019.101581"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8060672"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.32604\/csse.2023.028609"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-08053-z"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537\u2010023\u201000769\u20106"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-06922-1"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.105897"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tice.2021.101701"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3217217"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106624"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.3390\/app10175954"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3215547"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1111\/srt.13045"},{"key":"e_1_2_9_29_1","unstructured":"CauveryK SiddalingaswamyPC PathanS D'souzaN.A multiclass skin lesion classification approach using transfer learning based convolutional neural network. 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