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Skin lesion classification enhancement using border-line features – The melanoma vs nevus problem

2020-03Artigo científico Acesso aberto
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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosCiências Médicas::Outras Ciências Médicas
datacite.subject.fosEngenharia e Tecnologia::Engenharia Médica
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.authorFonseca-Pinto, Rui
dc.contributor.authorPaiva, Rui Pedro
dc.contributor.authorAssuncao, Pedro A. A.
dc.contributor.authorTavora, Luis M. N.
dc.contributor.authorThomaz, Lucas A.
dc.contributor.authorFaria, Sergio M. M.
dc.date.accessioned2025-10-07T11:16:11Z
dc.date.available2025-10-07T11:16:11Z
dc.date.issued2020-03
dc.description.abstractMachine learning algorithms are progressively assuming an important role as a computational tool to support clinical diagnosis, namely in the classification of pigmented skin lesions. The current classification methods commonly rely on features derived from shape, colour, or texture, obtained after image segmentation, but these do not always guarantee the best results. To improve the classification accuracy, this work proposes to further exploit the border-line characteristics of the lesion segmentation mask, by combining gradients with local binary patterns (LBP). In the proposed method, these border-line features are used together with the conventional ones to enhance the performance of skin lesion classification algorithms. When the new features are combined with the classical ones, the experimental results show higher accuracy, which impacts positively the overall performance of the classification algorithms. As the medical image datasets usually present large class imbalance, which results in low sensitivity for the classifiers, the border-line features have a positive impact on this classification metric, as evidenced by the experimental results. Both the features’ usefulness and their impact are assessed in regard to the classification results, which in turn are statistically tested for completeness, using three different classifiers and two medical image datasets.eng
dc.description.sponsorshipThis work was supported by the Fundação para a Ciência e Tecnologia, Portugal, under PhD Grant SFRH/BD/128669/2017 and project PlenoISLA PTDC/EEI-TEL/28325/2017, and Instituto de Telecomunicações project UID/EEA/50008/2019, through national funds and where applicable co-funded by FEDER – PT2020.
dc.identifier.citationPedro M.M. Pereira, Rui Fonseca-Pinto, Rui Pedro Paiva, Pedro A.A. Assuncao, Luis M.N. Tavora, Lucas A. Thomaz, Sergio M.M. Faria, Skin lesion classification enhancement using border-line features – The melanoma vs nevus problem, Biomedical Signal Processing and Control, Volume 57, 2020, 101765, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2019.101765.
dc.identifier.doi10.1016/j.bspc.2019.101765
dc.identifier.eissn1746-8108
dc.identifier.issn1746-8094
dc.identifier.urihttp://hdl.handle.net/10400.8/14220
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
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.relationPlenoptic imaging for skin lesion assessment
dc.relationInstituto de Telecomunicações
dc.relation.hasversionhttps://www.sciencedirect.com/science/article/pii/S1746809419303465?via%3Dihub
dc.relation.ispartofBiomedical Signal Processing and Control
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectMedical imaging
dc.subjectSkin lesion
dc.subjectImage segmentation
dc.subjectFeature extraction
dc.subjectClassification
dc.titleSkin lesion classification enhancement using border-line features – The melanoma vs nevus problemeng
dc.typejournal article
dspace.entity.typePublication
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.awardTitlePlenoptic imaging for skin lesion assessment
oaire.awardTitleInstituto de Telecomunicações
oaire.awardURIhttp://hdl.handle.net/10400.8/14125
oaire.awardURIhttp://hdl.handle.net/10400.8/14218
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F50008%2F2019/PT
oaire.citation.endPage8
oaire.citation.startPage1
oaire.citation.titleBiomedical Signal Processing and Control
oaire.citation.volume57
oaire.fundingStreamConcurso para Financiamento de Projetos de Investigação Científica e Desenvolvimento Tecnológico em Todos os Domínios Científicos - 2017
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
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person.familyNamede Oliveira Pegado de Noronha E Távora
person.familyNameThomaz
person.familyNameFaria
person.givenNameRui
person.givenNamePedro
person.givenNameLuís Miguel
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project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
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Machine learning algorithms are progressively assuming an important role as a computational tool to support clinical diagnosis, namely in the classification of pigmented skin lesions. The current classification methods commonly rely on features derived from shape, colour, or texture, obtained after image segmentation, but these do not always guarantee the best results. To improve the classification accuracy, this work proposes to further exploit the border-line characteristics of the lesion segmentation mask, by combining gradients with local binary patterns (LBP). In the proposed method, these border-line features are used together with the conventional ones to enhance the performance of skin lesion classification algorithms. When the new features are combined with the classical ones, the experimental results show higher accuracy, which impacts positively the overall performance of the classification algorithms. As the medical image datasets usually present large class imbalance, which results in low sensitivity for the classifiers, the border-line features have a positive impact on this classification metric, as evidenced by the experimental results. Both the features’ usefulness and their impact are assessed in regard to the classification results, which in turn are statistically tested for completeness, using three different classifiers and two medical image datasets.
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