{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T18:15:51Z","timestamp":1781201751065,"version":"3.54.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"36","license":[{"start":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T00:00:00Z","timestamp":1748995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T00:00:00Z","timestamp":1748995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100018777","name":"Nile University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100018777","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>With skin cancer rates increasing globally due to factors like prolonged ultraviolet exposure, early and accurate detection methods are crucial in managing and mitigating the disease. Implementing innovative diagnostic techniques can significantly enhance early intervention, improving patient outcomes and survival rates. This research aims to precisely identify and classify six skin cancer types. The proposed method integrates clinical images and metadata into a hybrid deep learning model, leveraging feature extraction and classification. The proposed approach employs seven machine learning algorithms alongside ten pre-trained deep learning models, notably featuring MobileNetV2 for image feature extraction, logistic regression for image classification, and random forest for metadata analysis. The developed approach is tested with the PAD-UFES-20 dataset and it demonstrate a notable improvement in diagnostic accuracy (95.6%), precision (96.8%), recall (95.6%), and F1-Score (95.7%). This findings highlight the significant contribution of metadata in enhancing classification accuracy, surpassing image-based methods alone.<\/jats:p>","DOI":"10.1007\/s11042-025-20951-7","type":"journal-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T06:34:16Z","timestamp":1749018856000},"page":"45321-45345","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["MiSC: A hybrid multi-modal deep learning approach for accurate skin cancer detection"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1615-0236","authenticated-orcid":false,"given":"Ensaf Hussein","family":"Mohamed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nada","family":"Abdu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mostafa","family":"Khalil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hossam","family":"Kamal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Essam A.","family":"Rashed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,4]]},"reference":[{"key":"20951_CR1","unstructured":"(2023) American cancer society: Cancer facts & figures 2023. Report, American Cancer Society, Atlanta, GA"},{"key":"20951_CR2","volume-title":"Global cancer statistics 2023","author":"World Health Organization","year":"2023","unstructured":"World Health Organization (2023) Global cancer statistics 2023. Report, World Health Organization, Geneva, Switzerland"},{"issue":"1","key":"20951_CR3","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.ijwd.2020.07.003","volume":"7","author":"ER Parker","year":"2021","unstructured":"Parker ER (2021) The influence of climate change on skin cancer incidence-a review of the evidence. Int J Women\u2019s Derma 7(1):17\u201327","journal-title":"Int J Women\u2019s Derma"},{"key":"20951_CR4","first-page":"20","volume":"6271","author":"K Orthaber","year":"2017","unstructured":"Orthaber K, Pristovnik M, Skok K, Peri\u0107 B, Maver U (2017) Skin cancer and its treatment: Novel treatment approaches with emphasis on nanotechnology. J Nanomater 6271:20","journal-title":"J Nanomater"},{"issue":"1","key":"20951_CR5","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s00403-008-0894-6","volume":"301","author":"ME Celebi","year":"2009","unstructured":"Celebi ME et al (2009) Methodological approaches to the classification of dermoscopic images. Arch Dermatol Res 301(1):93\u2013101","journal-title":"Arch Dermatol Res"},{"issue":"2","key":"20951_CR6","first-page":"175","volume":"39","author":"K Gardner","year":"2011","unstructured":"Gardner K et al (2011) Decision trees for classification of dermatological lesions. J Clin Derma 39(2):175\u2013183","journal-title":"J Clin Derma"},{"issue":"3","key":"20951_CR7","first-page":"520","volume":"111","author":"B Harwood","year":"2013","unstructured":"Harwood B et al (2013) Ensemble methods for classification of skin lesions through image features. Comput Methods Programs Biomed 111(3):520\u2013527","journal-title":"Comput Methods Programs Biomed"},{"issue":"1","key":"20951_CR8","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.media.2013.10.003","volume":"18","author":"T Mendonca","year":"2014","unstructured":"Mendonca T et al (2014) Dimensionality reduction techniques for improved skin lesion classification. Med Image Anal 18(1):130\u2013137","journal-title":"Med Image Anal"},{"issue":"4","key":"20951_CR9","first-page":"569","volume":"41","author":"G Fabbrocini","year":"2015","unstructured":"Fabbrocini G et al (2015) Classification of skin lesions using artificial neural networks. Dermatol Surg 41(4):569\u2013573","journal-title":"Dermatol Surg"},{"key":"20951_CR10","doi-asserted-by":"crossref","unstructured":"Junayed MS, Anjum N, Sakib ANM, Islam M (2021) A deep cnn model for skin cancer detection and classification. J WSCG.","DOI":"10.24132\/CSRN.2021.3002.8"},{"key":"20951_CR11","unstructured":"Krohling B, Castro PB, Pacheco AG, Krohling RA (2021) A smartphone based application for skin cancer classification using deep learning with clinical images and lesion information. arXiv:2104.14353"},{"key":"20951_CR12","doi-asserted-by":"crossref","unstructured":"Saini S, Jeon YS, Feng M (2021) B-segnet: branched-segmentor network for skin lesion segmentation. Proc Conf Health Infer Learn (CHIL) 10:1145","DOI":"10.1145\/3450439.3451873"},{"issue":"17","key":"20951_CR13","doi-asserted-by":"publisher","first-page":"26255","DOI":"10.1007\/s11042-021-10952-7","volume":"80","author":"MM Mijwil","year":"2021","unstructured":"Mijwil MM (2021) Skin cancer disease images classification using deep learning solutions. 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This study was carried out on a public data set prepared and shared by Mendeley Data [\n                      \n                      ]. The data was collected in collaboration with the Federal University of Espirito Santo\u2019s Dermatological and Surgical Assistance Program (PAD).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}