Multidimensional shannon entropy (HM) as an approach to classify H&E colorectal images

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.creatorSantos, Luiz Fernando Segato Dos-
Autor(es): dc.creatorRozendo, Guilherme Botazzo-
Autor(es): dc.creatorNascimento, Marcelo Zanchetta Do-
Autor(es): dc.creatorTosta, Thaina Aparecida Azevedo-
Autor(es): dc.creatorLongo, Leonardo Henrique Da Costa-
Autor(es): dc.creatorNeves, Leandro Alves-
Data de aceite: dc.date.accessioned2025-08-21T20:34:57Z-
Data de disponibilização: dc.date.available2025-08-21T20:34:57Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/IWSSIP55020.2022.9854438-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/241596-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/241596-
Descrição: dc.descriptionIn this work, we have proposed a method that combines multiscale and multidimensional approaches with Shannon entropy, named HM. The method was combined with other fractal and sample entropy techniques and tested on H&E colorectal images. The results provided an accuracy of 95.36% for the combination HM and SampEnMF. The combinations and analyses presented here are important contributions to the Literature focused on the investigation of techniques for the development of computer-aided diagnosis.-
Descrição: dc.descriptionSao Paulo State University (UNESP) Department of Computer Science and Statistics (DCCE)-
Descrição: dc.descriptionFederal University of Uberlândia (UFU) Faculty of Computer Science (FACOM)-
Descrição: dc.descriptionScience and Technology Institute Federal University of São Paulo (UNIFESP)-
Descrição: dc.descriptionSao Paulo State University (UNESP) Department of Computer Science and Statistics (DCCE)-
Idioma: dc.languageen-
Relação: dc.relationInternational Conference on Systems, Signals, and Image Processing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectcolorectal images-
Palavras-chave: dc.subjectcombination-
Palavras-chave: dc.subjectmultidimensional-
Palavras-chave: dc.subjectmultiscale-
Palavras-chave: dc.subjectshannon entropy-
Título: dc.titleMultidimensional shannon entropy (HM) as an approach to classify H&E colorectal images-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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