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

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dc.contributor.authorSantos, Luiz Fernando Segato Dos [UNESP]
dc.contributor.authorRozendo, Guilherme Botazzo [UNESP]
dc.contributor.authorNascimento, Marcelo Zanchetta Do
dc.contributor.authorTosta, Thaina Aparecida Azevedo
dc.contributor.authorLongo, Leonardo Henrique Da Costa [UNESP]
dc.contributor.authorNeves, Leandro Alves [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversidade Federal de Uberlândia (UFU)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.date.accessioned2023-03-01T21:12:05Z
dc.date.available2023-03-01T21:12:05Z
dc.date.issued2022-01-01
dc.description.abstractIn 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.en
dc.description.affiliationSao Paulo State University (UNESP) Department of Computer Science and Statistics (DCCE)
dc.description.affiliationFederal University of Uberlândia (UFU) Faculty of Computer Science (FACOM)
dc.description.affiliationScience and Technology Institute Federal University of São Paulo (UNIFESP)
dc.description.affiliationUnespSao Paulo State University (UNESP) Department of Computer Science and Statistics (DCCE)
dc.identifierhttp://dx.doi.org/10.1109/IWSSIP55020.2022.9854438
dc.identifier.citationInternational Conference on Systems, Signals, and Image Processing, v. 2022-June.
dc.identifier.dimensionspub.1150297210
dc.identifier.doi10.1109/IWSSIP55020.2022.9854438
dc.identifier.isbn978-1-6654-9578-3
dc.identifier.issn2157-8702
dc.identifier.issn2157-8672
dc.identifier.orcid0000-0003-3537-0178
dc.identifier.orcid0000-0002-4123-8264
dc.identifier.orcid0000-0001-8580-7054
dc.identifier.orcid0000-0002-9291-8892
dc.identifier.scopus2-s2.0-85137169801
dc.identifier.urihttp://hdl.handle.net/11449/241596
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofInternational Conference on Systems, Signals, and Image Processing
dc.rights.accessRightsAcesso restritopt
dc.rights.sourceRightsclosed
dc.sourceScopus
dc.sourceDimensions
dc.subjectcolorectal images
dc.subjectcombination
dc.subjectmultidimensional
dc.subjectmultiscale
dc.subjectshannon entropy
dc.titleMultidimensional shannon entropy (HM) as an approach to classify H&E colorectal imagesen
dc.typeTrabalho apresentado em eventopt
dspace.entity.typePublication
relation.isOrgUnitOfPublication43c38943-bd6f-4fb6-a9a5-8482a1f632c0
relation.isOrgUnitOfPublication.latestForDiscovery43c38943-bd6f-4fb6-a9a5-8482a1f632c0
unesp.campusUniversidade Estadual Paulista (UNESP), Instituto de Biociências Letras e Ciências Exatas, São José do Rio Pretopt
unesp.departmentCiências da Computação e Estatística - IBILCEpt

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São José do Rio Preto - IBILCE - Instituto de Biociências, Letras e Ciências Exatas