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An evaluation of image preprocessing in skin lesions detection

datacite.subject.fosEngenharia e Tecnologia
dc.contributor.authorSilva, Giuliana
dc.contributor.authorLazzaretti, André
dc.contributor.authorMonteiro, Fernando C.
dc.date.accessioned2026-05-18T13:42:45Z
dc.date.available2026-05-18T13:42:45Z
dc.date.issued2023
dc.description.abstractThis study aims to evaluate the impact of image preprocessing techniques on the performance of Convolutional Neural Network (CNNs) in the task of skin lesion classification. The study is made on the ISIC 2017 dataset, a widely used resource in skin cancer diagnosis research. Thirteen popular CNN models were trained using transfer learning. An ensemble strategy was also employed to generate a final diagnosis based on the classifications of different models. The results indicate that image preprocessing can significantly enhance the performance of CNN models in skin lesion classification tasks. Our best model obtained a balanced accuracy of 0.7879.eng
dc.identifier.citationSilva, Giuliana; Lazzaretti, André; Monteiro, Fernando C. (2023). An evaluation of image preprocessing in skin lesions detection. In OL2A 2023, Ponta Delgada, Portugal. ISBN 978-972-745-326-9
dc.identifier.isbn978-972-745-326-9
dc.identifier.urihttp://hdl.handle.net/10198/36707
dc.language.isoeng
dc.peerreviewedyes
dc.relation.hasversionhttps://ol2a.ipb.pt/ui/assets/books_abstracts/Book_Abstracts_OL2A_Final_2023.pdf
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleAn evaluation of image preprocessing in skin lesions detectioneng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2023
oaire.citation.conferencePlacePonta Delgada, Portugal
oaire.citation.endPage57
oaire.citation.startPage57
oaire.citation.titleOL2A 2023
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameMonteiro
person.givenNameFernando C.
person.identifier.ciencia-id2019-BDBF-10E2
person.identifier.orcid0000-0002-1421-8006
person.identifier.ridH-9213-2016
person.identifier.scopus-author-id8986162600
relation.isAuthorOfPublication363b6c37-282c-4cd6-bb54-3c97cc700d78
relation.isAuthorOfPublication.latestForDiscovery363b6c37-282c-4cd6-bb54-3c97cc700d78

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