Computational diagnosis of skin lesions from dermoscopic images using combined features

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniv Porto-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorOliveira, Roberta B.-
Autor(es): dc.creatorPereira, Aledir S. [UNESP]-
Autor(es): dc.creatorTavares, Joao Manuel R. S.-
Data de aceite: dc.date.accessioned2022-02-22T00:08:55Z-
Data de disponibilização: dc.date.available2022-02-22T00:08:55Z-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2019-10-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s00521-018-3439-8-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/196252-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/196252-
Descrição: dc.descriptionThere has been an alarming increase in the number of skin cancer cases worldwide in recent years, which has raised interest in computational systems for automatic diagnosis to assist early diagnosis and prevention. Feature extraction to describe skin lesions is a challenging research area due to the difficulty in selecting meaningful features. The main objective of this work is to find the best combination of features, based on shape properties, colour variation and texture analysis, to be extracted using various feature extraction methods. Several colour spaces are used for the extraction of both colour- and texture-related features. Different categories of classifiers were adopted to evaluate the proposed feature extraction step, and several feature selection algorithms were compared for the classification of skin lesions. The developed skin lesion computational diagnosis system was applied to a set of 1104 dermoscopic images using a cross-validation procedure. The best results were obtained by an optimum-path forest classifier with very promising results. The proposed system achieved an accuracy of 92.3%, sensitivity of 87.5% and specificity of 97.1% when the full set of features was used. Furthermore, it achieved an accuracy of 91.6%, sensitivity of 87% and specificity of 96.2%, when 50 features were selected using a correlation-based feature selection algorithm.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionPrograma Operacional Regional do Norte (NORTE2020), through Fundo Europeu de Desenvolvimento Regional (FEDER)-
Descrição: dc.descriptionUniv Porto, Fac Engn, Inst Ciencia & Inovacao Engn Mecan & Engn Ind, Dept Engn Mecan, Rua Dr Roberto Frias, P-4200465 Porto, Portugal-
Descrição: dc.descriptionUniv Estadual Paulista, Inst Biociencias Letras & Ciencias Exatas, Dept Ciencias Comp & Estat, Rua Cristovao Colombo 2265, BR-15054000 Sao Jose Do Rio Preto, SP, Brazil-
Descrição: dc.descriptionUniv Estadual Paulista, Inst Biociencias Letras & Ciencias Exatas, Dept Ciencias Comp & Estat, Rua Cristovao Colombo 2265, BR-15054000 Sao Jose Do Rio Preto, SP, Brazil-
Descrição: dc.descriptionPrograma Operacional Regional do Norte (NORTE2020), through Fundo Europeu de Desenvolvimento Regional (FEDER): NORTE-01-0145-FEDER-000022-
Formato: dc.format6091-6111-
Idioma: dc.languageen-
Publicador: dc.publisherSpringer-
Relação: dc.relationNeural Computing & Applications-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectFeature extraction and selection-
Palavras-chave: dc.subjectFractal dimension analysis-
Palavras-chave: dc.subjectDiscrete wavelet transform-
Palavras-chave: dc.subjectCo-occurrence matrix-
Título: dc.titleComputational diagnosis of skin lesions from dermoscopic images using combined features-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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