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

Detalhes bibliográficos
Autor(a) principal: Santos, Luiz Fernando Segato Dos [UNESP]
Data de Publicação: 2022
Outros Autores: Rozendo, Guilherme Botazzo [UNESP], Nascimento, Marcelo Zanchetta Do, Tosta, Thaina Aparecida Azevedo, Longo, Leonardo Henrique Da Costa [UNESP], Neves, Leandro Alves [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/IWSSIP55020.2022.9854438
http://hdl.handle.net/11449/241596
Resumo: In 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.
id UNSP_3b82a37272422267073d18b329587f7d
oai_identifier_str oai:repositorio.unesp.br:11449/241596
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling Multidimensional shannon entropy (HM) as an approach to classify H&E colorectal imagescolorectal imagescombinationmultidimensionalmultiscaleshannon entropyIn 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.Sao Paulo State University (UNESP) Department of Computer Science and Statistics (DCCE)Federal University of Uberlândia (UFU) Faculty of Computer Science (FACOM)Science and Technology Institute Federal University of São Paulo (UNIFESP)Sao Paulo State University (UNESP) Department of Computer Science and Statistics (DCCE)Universidade Estadual Paulista (UNESP)Universidade Federal de Uberlândia (UFU)Universidade de São Paulo (USP)Santos, Luiz Fernando Segato Dos [UNESP]Rozendo, Guilherme Botazzo [UNESP]Nascimento, Marcelo Zanchetta DoTosta, Thaina Aparecida AzevedoLongo, Leonardo Henrique Da Costa [UNESP]Neves, Leandro Alves [UNESP]2023-03-01T21:12:05Z2023-03-01T21:12:05Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/IWSSIP55020.2022.9854438International Conference on Systems, Signals, and Image Processing, v. 2022-June.2157-87022157-8672http://hdl.handle.net/11449/24159610.1109/IWSSIP55020.2022.98544382-s2.0-85137169801Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Conference on Systems, Signals, and Image Processinginfo:eu-repo/semantics/openAccess2023-03-01T21:12:05Zoai:repositorio.unesp.br:11449/241596Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:27:12.539130Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Multidimensional shannon entropy (HM) as an approach to classify H&E colorectal images
title Multidimensional shannon entropy (HM) as an approach to classify H&E colorectal images
spellingShingle Multidimensional shannon entropy (HM) as an approach to classify H&E colorectal images
Santos, Luiz Fernando Segato Dos [UNESP]
colorectal images
combination
multidimensional
multiscale
shannon entropy
title_short Multidimensional shannon entropy (HM) as an approach to classify H&E colorectal images
title_full Multidimensional shannon entropy (HM) as an approach to classify H&E colorectal images
title_fullStr Multidimensional shannon entropy (HM) as an approach to classify H&E colorectal images
title_full_unstemmed Multidimensional shannon entropy (HM) as an approach to classify H&E colorectal images
title_sort Multidimensional shannon entropy (HM) as an approach to classify H&E colorectal images
author Santos, Luiz Fernando Segato Dos [UNESP]
author_facet Santos, Luiz Fernando Segato Dos [UNESP]
Rozendo, Guilherme Botazzo [UNESP]
Nascimento, Marcelo Zanchetta Do
Tosta, Thaina Aparecida Azevedo
Longo, Leonardo Henrique Da Costa [UNESP]
Neves, Leandro Alves [UNESP]
author_role author
author2 Rozendo, Guilherme Botazzo [UNESP]
Nascimento, Marcelo Zanchetta Do
Tosta, Thaina Aparecida Azevedo
Longo, Leonardo Henrique Da Costa [UNESP]
Neves, Leandro Alves [UNESP]
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
Universidade Federal de Uberlândia (UFU)
Universidade de São Paulo (USP)
dc.contributor.author.fl_str_mv Santos, Luiz Fernando Segato Dos [UNESP]
Rozendo, Guilherme Botazzo [UNESP]
Nascimento, Marcelo Zanchetta Do
Tosta, Thaina Aparecida Azevedo
Longo, Leonardo Henrique Da Costa [UNESP]
Neves, Leandro Alves [UNESP]
dc.subject.por.fl_str_mv colorectal images
combination
multidimensional
multiscale
shannon entropy
topic colorectal images
combination
multidimensional
multiscale
shannon entropy
description In 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.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
2023-03-01T21:12:05Z
2023-03-01T21:12:05Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/IWSSIP55020.2022.9854438
International Conference on Systems, Signals, and Image Processing, v. 2022-June.
2157-8702
2157-8672
http://hdl.handle.net/11449/241596
10.1109/IWSSIP55020.2022.9854438
2-s2.0-85137169801
url http://dx.doi.org/10.1109/IWSSIP55020.2022.9854438
http://hdl.handle.net/11449/241596
identifier_str_mv International Conference on Systems, Signals, and Image Processing, v. 2022-June.
2157-8702
2157-8672
10.1109/IWSSIP55020.2022.9854438
2-s2.0-85137169801
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv International Conference on Systems, Signals, and Image Processing
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808129203938263040

Registros relacionados