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Deep learning techniques applied to skin lesion classification: a review

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dc.contributor.authorSilva, Giuliana Martins
dc.contributor.authorLazzaretti, Andre E.
dc.contributor.authorMonteiro, Fernando C.
dc.date.accessioned2023-03-09T14:25:45Z
dc.date.available2023-03-09T14:25:45Z
dc.date.issued2022
dc.description.abstractSkin cancer is one of the most common cancers in the world. The most dangerous type of skin cancer is melanoma, which can be lethal if not treated early. However, diagnosing skin lesions can be a difficult task. Therefore, deep learning techniques applied to the diagnosis of skin lesions have been explored by researchers, given their effectiveness in extracting features and classifying input data. In this work, we present a review of latest approaches that apply deep learning techniques to skin lesion classification task. In addition, some datasets used for training and validating the models are introduced, informing their characteristics and specificities, as well as popular pre-processing steps and skin lesion segmentation approaches. Finally, we comment the effectiveness of the proposed models.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSilva, Giuliana M.; Lazzaretti, Andre E.; Monteiro, Fernando C. (2022). Deep learning techniques applied to skin lesion classification: a review. In International Conference on Machine Learning, Control, and Robotics, MLCR 2022. Suzhou - China. p. 106 - 111pt_PT
dc.identifier.doi10.1109/MLCR57210.2022.00028pt_PT
dc.identifier.isbn978-166545459-9
dc.identifier.urihttp://hdl.handle.net/10198/27583
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEE Xplorept_PT
dc.relationLA/P/0007/2021pt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectDeep learningpt_PT
dc.subjectMelanomapt_PT
dc.subjectSkin diseasespt_PT
dc.subjectSkin lesion classificationpt_PT
dc.subjectSkin lesion segmentationpt_PT
dc.titleDeep learning techniques applied to skin lesion classification: a reviewpt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.citation.conferencePlaceSuzhou - Chinapt_PT
oaire.citation.endPage111pt_PT
oaire.citation.startPage106pt_PT
oaire.citation.title2022 International Conference on Machine Learning, Control, and Robotics (MLCR)pt_PT
oaire.fundingStream6817 - DCRRNI ID
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
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication363b6c37-282c-4cd6-bb54-3c97cc700d78
relation.isAuthorOfPublication.latestForDiscovery363b6c37-282c-4cd6-bb54-3c97cc700d78
relation.isProjectOfPublication6e01ddc8-6a82-4131-bca6-84789fa234bd
relation.isProjectOfPublication.latestForDiscovery6e01ddc8-6a82-4131-bca6-84789fa234bd

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