Logo do repositório
 
Publicação

Skin lesion classification using features of 3D border lines

2021-11Comunicação em conferência Acesso aberto
Ver registo simples
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg10:Reduzir as Desigualdades
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
dc.contributor.authorPereira, Pedro M. M.
dc.contributor.authorThomaz, Lucas A.
dc.contributor.authorTavora, Luis M. N.
dc.contributor.authorAssuncao, Pedro A. A.
dc.contributor.authorFonseca-Pinto, Rui
dc.contributor.authorPaiva, Rui Pedro
dc.contributor.authorFaria, Sergio M. M.
dc.date.accessioned2026-04-27T14:36:13Z
dc.date.available2026-04-27T14:36:13Z
dc.date.issued2021-11
dc.description43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021; Conference date - 1 November 2021 - 5 November 2021; Conference code - 175446
dc.descriptionEISBN - 978-1-7281-1179-7
dc.descriptionLink de acesso ao documento - https://eden.dei.uc.pt/~ruipedro/publications/Conferences/EMBC_2021_Pereira.pdf
dc.description.abstractMachine learning algorithms are progressively assuming important roles as computational tools to support clinical diagnosis, namely in the classification of pigmented skin lesions using RGB images. Most current classification methods rely on common 2D image features derived from shape, colour or texture, which does not always guarantee the best results. This work presents a contribution to this field, by exploiting the lesions' border line characteristics using a new dimension - depth, which has not been thoroughly investigated so far. A selected group of features is extracted from the depth information of 3D images, which are then used for classification using a quadratic Support Vector Machine. Despite class imbalance often present in medical image datasets, the proposed algorithm achieves a top geometric mean of 94.87%, comprising 100.00% sensitivity and 90.00% specificity, using only depth information for the detection of Melanomas. Such results show that potential gains can be achieved by extracting information from this often overlooked dimension, which provides more balanced results in terms of sensitivity and specificity than other settings.eng
dc.description.sponsorshipThis work was supported by the Fundação para a Ciência e a Tecnologia (FCT), Portugal, under PhD Grant SFRH/BD/128669/2017, Programa Operacional Regional do Centro, project PlenoISLA POCI-01-0145-FEDER-028325 and by FCT/MCTES through national funds and when applicable co-funded by EU funds under the project UIDB/EEA/50008/2020.
dc.identifier.citationP. M. M. Pereira et al., "Skin lesion classification using features of 3D border lines," 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Mexico, 2021, pp. 2726-2731, doi: https://doi.org/10.1109/EMBC46164.2021.9629966.
dc.identifier.doi10.1109/embc46164.2021.9629966
dc.identifier.eissn2694-0604
dc.identifier.isbn978-1-7281-1180-3
dc.identifier.isbn978-1-7281-1179-7
dc.identifier.issn2375-7477
dc.identifier.urihttp://hdl.handle.net/10400.8/16205
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE Canada
dc.relationSkin Lesion Assessment based on Plenoptic Images for Melanoma Classification. Titulo anterior: Texture and Patterns in 3D plenoptic skin lesion assessment: A taxonomy proposal for melanoma classification
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/9629966
dc.relation.ispartof2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
dc.rights.uriN/A
dc.subjectSupport vector machines
dc.subjectThree-dimensional displays
dc.subjectSensitivity
dc.subjectShape
dc.subjectTools
dc.subjectSensitivity and specificity
dc.subjectFeature extraction
dc.titleSkin lesion classification using features of 3D border lineseng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardNumberSFRH/BD/128669/2017
oaire.awardTitleSkin Lesion Assessment based on Plenoptic Images for Melanoma Classification. Titulo anterior: Texture and Patterns in 3D plenoptic skin lesion assessment: A taxonomy proposal for melanoma classification
oaire.awardURIhttp://hdl.handle.net/10400.8/14125
oaire.citation.conferenceDate2021-11
oaire.citation.conferencePlaceMexico, Mexico
oaire.citation.endPage2731
oaire.citation.startPage2726
oaire.citation.titleAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameThomaz
person.familyNamede Oliveira Pegado de Noronha E Távora
person.familyNameAssunção
person.familyNameFonseca-Pinto
person.familyNameFaria
person.givenNameLucas
person.givenNameLuís Miguel
person.givenNamePedro
person.givenNameRui
person.givenNameSergio
person.identifier.ciencia-id121C-FADA-D750
person.identifier.ciencia-id6811-3984-C17B
person.identifier.ciencia-id681D-C547-B184
person.identifier.ciencia-id8815-4101-28DD
person.identifier.orcid0000-0002-1004-7772
person.identifier.orcid0000-0002-8580-1979
person.identifier.orcid0000-0001-9539-8311
person.identifier.orcid0000-0001-6774-5363
person.identifier.orcid0000-0002-0993-9124
person.identifier.ridA-4827-2017
person.identifier.ridK-9449-2014
person.identifier.ridC-5245-2011
person.identifier.scopus-author-id6701838347
person.identifier.scopus-author-id26039086400
person.identifier.scopus-author-id14027853900
relation.isAuthorOfPublication5aaecc29-3e2f-49a6-9fd9-7000d9f6085f
relation.isAuthorOfPublication71940f24-f333-4ab6-abf6-00c7119a07c2
relation.isAuthorOfPublication25649bb9-f135-48e8-8d0f-3706b86701d3
relation.isAuthorOfPublication7eb9d123-1800-4afd-a2f6-91043353011b
relation.isAuthorOfPublicationf69bd4d6-a6ef-4d20-8148-575478909661
relation.isAuthorOfPublication.latestForDiscovery5aaecc29-3e2f-49a6-9fd9-7000d9f6085f
relation.isProjectOfPublication90b7c55a-b84e-4987-821f-cb7eb07499de
relation.isProjectOfPublication.latestForDiscovery90b7c55a-b84e-4987-821f-cb7eb07499de

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura
Nome:
Skin lesion classification using features of 3D border lines.pdf
Tamanho:
1.66 MB
Formato:
Adobe Portable Document Format
Descrição:
Machine learning algorithms are progressively assuming important roles as computational tools to support clinical diagnosis, namely in the classification of pigmented skin lesions using RGB images. Most current classification methods rely on common 2D image features derived from shape, colour or texture, which does not always guarantee the best results. This work presents a contribution to this field, by exploiting the lesions' border line characteristics using a new dimension - depth, which has not been thoroughly investigated so far. A selected group of features is extracted from the depth information of 3D images, which are then used for classification using a quadratic Support Vector Machine. Despite class imbalance often present in medical image datasets, the proposed algorithm achieves a top geometric mean of 94.87%, comprising 100.00% sensitivity and 90.00% specificity, using only depth information for the detection of Melanomas. Such results show that potential gains can be achieved by extracting information from this often overlooked dimension, which provides more balanced results in terms of sensitivity and specificity than other settings.
Ver/Abrir
Licença
A mostrar 1 - 1 de 1
Miniatura indisponível
Nome:
license.txt
Tamanho:
1.32 KB
Formato:
Item-specific license agreed upon to submission
Descrição:
Ver/Abrir

Coleções

ESTG - Comunicações em conferências e congressos internacionais