Combining heterogeneous data and deep learning models for skin cancer detection

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dc.contributor.advisor1Krohling, Renato Antonio
dc.contributor.advisor1IDhttps://orcid.org/
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/5300435085221378
dc.contributor.authorPacheco, Andre Georghton Cardoso
dc.contributor.authorIDhttps://orcid.org/
dc.contributor.authorLatteshttp://lattes.cnpq.br/
dc.contributor.referee1Mota, Vinicius Fernandes Soares
dc.contributor.referee1IDhttps://orcid.org/
dc.contributor.referee1Latteshttp://lattes.cnpq.br/9305955394665920
dc.contributor.referee2Cavalieri, Daniel Cruz
dc.contributor.referee2IDhttps://orcid.org/
dc.contributor.referee2Latteshttp://lattes.cnpq.br/
dc.contributor.referee3Papa, Joao Paulo
dc.contributor.referee3IDhttps://orcid.org/
dc.contributor.referee3Latteshttp://lattes.cnpq.br/
dc.contributor.referee4Santos, Celso Alberto Saibel
dc.contributor.referee4IDhttps://orcid.org/0000000232875843
dc.contributor.referee4Latteshttp://lattes.cnpq.br/7614206164174151
dc.date.accessioned2024-05-30T00:49:10Z
dc.date.available2024-05-30T00:49:10Z
dc.date.issued2020-11-12
dc.description.abstractCurrently, Deep Neural Networks (DNN) are the most successful and common methodologies to tackle medical image analysis. Despite the success, applying Deep Learning for these types of problems involves several challenges such as the lack of large training
dc.description.resumoCurrently, Deep Neural Networks (DNN) are the most successful and common methodologies to tackle medical image analysis. Despite the success, applying Deep Learning for these types of problems involves several challenges such as the lack of large training
dc.description.sponsorshipFundação Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.formatText
dc.identifier.urihttp://repositorio.ufes.br/handle/10/14433
dc.languagepor
dc.publisherUniversidade Federal do Espírito Santo
dc.publisher.countryBR
dc.publisher.courseDoutorado em Ciência da Computação
dc.publisher.departmentCentro Tecnológico
dc.publisher.initialsUFES
dc.publisher.programPrograma de Pós-Graduação em Informática
dc.rightsopen access
dc.subjectPalavra-chave
dc.subject.br-rjbnsubject.br-rjbn
dc.subject.cnpqCiência da Computação
dc.titleCombining heterogeneous data and deep learning models for skin cancer detection
dc.title.alternativeCombining heterogeneous data and deep learning models for skin cancer detection
dc.typedoctoralThesis
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