Analysis of cancer in histological images: employing an approach based on genetic algorithm
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s10044-020-00931-3 http://hdl.handle.net/11449/206801 |
Resumo: | The analysis of histological images is based on visual assessment of tissues by specialists using an optical microscopy. This task can be time-consuming and challenging, mainly due to the complexity of the structures and diseases under investigation. These facts have motivated the development of computational methods to support specialists in research and decision-making. Despite the different computational strategies available in the literature, the solutions based on genetic algorithm have not been fully explored to provide the best combination of features, selection algorithms and classifiers. In this paper, we describe an approach based on genetic algorithm able to evaluate a significant number of features, selection methods and classifiers in order to provide an acceptable association for the diagnosis and pattern recognition of non-Hodgkin lymphomas and colorectal cancer. The chromosomal structure was represented with four genes. The evaluation and selection of individuals, as well as the crossover and mutation processes, were defined to distinguish the groups under investigation, with the highest AUC value and the smallest number of features. The tests were performed considering 1512 features from histological images, different population sizes and number of iterations. An initial population of 50 individuals and 50 iterations provided the best result (AUC value of 0.984) for the colorectal histological images. For non-Hodgkin lymphoma images, the best result (AUC value of 0.947) was obtained with a population of 500 individuals and 50 iterations. The proposed methodology with detailed information regarding the methods, features and best associations are relevant contributions for the community interested in the study of pattern recognition of colorectal cancer and lymphomas. |
id |
UNSP_b1c5d5b38274b3e57cdb7c64723ce3cd |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/206801 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
Analysis of cancer in histological images: employing an approach based on genetic algorithmBest associationClassificationColorectal cancerFeature selectionGenetic algorithmNon-Hodgkin lymphomaThe analysis of histological images is based on visual assessment of tissues by specialists using an optical microscopy. This task can be time-consuming and challenging, mainly due to the complexity of the structures and diseases under investigation. These facts have motivated the development of computational methods to support specialists in research and decision-making. Despite the different computational strategies available in the literature, the solutions based on genetic algorithm have not been fully explored to provide the best combination of features, selection algorithms and classifiers. In this paper, we describe an approach based on genetic algorithm able to evaluate a significant number of features, selection methods and classifiers in order to provide an acceptable association for the diagnosis and pattern recognition of non-Hodgkin lymphomas and colorectal cancer. The chromosomal structure was represented with four genes. The evaluation and selection of individuals, as well as the crossover and mutation processes, were defined to distinguish the groups under investigation, with the highest AUC value and the smallest number of features. The tests were performed considering 1512 features from histological images, different population sizes and number of iterations. An initial population of 50 individuals and 50 iterations provided the best result (AUC value of 0.984) for the colorectal histological images. For non-Hodgkin lymphoma images, the best result (AUC value of 0.947) was obtained with a population of 500 individuals and 50 iterations. The proposed methodology with detailed information regarding the methods, features and best associations are relevant contributions for the community interested in the study of pattern recognition of colorectal cancer and lymphomas.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)Department of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265Faculty of Computer Science (FACOM) - Federal University of Uberlândia (UFU), Avenida João Neves de Ávila 2121, Bl.BCenter of Mathematics Computing and Cognition Federal University of ABC (UFABC), Avenida dos Estados, 5001Federal Institute of Triângulo Mineiro (IFTM), Rua Belarmino Vilela Junqueira snDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265CNPq: #304848/2018-2CNPq: #313365/2018-0CNPq: #427114/2016-0CNPq: #430965/2018-4FAPEMIG: #APQ-00578-18Universidade Estadual Paulista (Unesp)Universidade Federal de Uberlândia (UFU)Universidade Federal do ABC (UFABC)Federal Institute of Triângulo Mineiro (IFTM)Taino, Daniela F. [UNESP]Ribeiro, Matheus G. [UNESP]Roberto, Guilherme F.Zafalon, Geraldo F. D. [UNESP]do Nascimento, Marcelo Z.Tosta, Thaína A. A.Martins, Alessandro S.Neves, Leandro A. [UNESP]2021-06-25T10:44:05Z2021-06-25T10:44:05Z2021-05-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article483-496http://dx.doi.org/10.1007/s10044-020-00931-3Pattern Analysis and Applications, v. 24, n. 2, p. 483-496, 2021.1433-755X1433-7541http://hdl.handle.net/11449/20680110.1007/s10044-020-00931-32-s2.0-85095701837Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPattern Analysis and Applicationsinfo:eu-repo/semantics/openAccess2021-10-23T15:25:05Zoai:repositorio.unesp.br:11449/206801Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T15:25:05Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Analysis of cancer in histological images: employing an approach based on genetic algorithm |
title |
Analysis of cancer in histological images: employing an approach based on genetic algorithm |
spellingShingle |
Analysis of cancer in histological images: employing an approach based on genetic algorithm Taino, Daniela F. [UNESP] Best association Classification Colorectal cancer Feature selection Genetic algorithm Non-Hodgkin lymphoma |
title_short |
Analysis of cancer in histological images: employing an approach based on genetic algorithm |
title_full |
Analysis of cancer in histological images: employing an approach based on genetic algorithm |
title_fullStr |
Analysis of cancer in histological images: employing an approach based on genetic algorithm |
title_full_unstemmed |
Analysis of cancer in histological images: employing an approach based on genetic algorithm |
title_sort |
Analysis of cancer in histological images: employing an approach based on genetic algorithm |
author |
Taino, Daniela F. [UNESP] |
author_facet |
Taino, Daniela F. [UNESP] Ribeiro, Matheus G. [UNESP] Roberto, Guilherme F. Zafalon, Geraldo F. D. [UNESP] do Nascimento, Marcelo Z. Tosta, Thaína A. A. Martins, Alessandro S. Neves, Leandro A. [UNESP] |
author_role |
author |
author2 |
Ribeiro, Matheus G. [UNESP] Roberto, Guilherme F. Zafalon, Geraldo F. D. [UNESP] do Nascimento, Marcelo Z. Tosta, Thaína A. A. Martins, Alessandro S. Neves, Leandro A. [UNESP] |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade Federal de Uberlândia (UFU) Universidade Federal do ABC (UFABC) Federal Institute of Triângulo Mineiro (IFTM) |
dc.contributor.author.fl_str_mv |
Taino, Daniela F. [UNESP] Ribeiro, Matheus G. [UNESP] Roberto, Guilherme F. Zafalon, Geraldo F. D. [UNESP] do Nascimento, Marcelo Z. Tosta, Thaína A. A. Martins, Alessandro S. Neves, Leandro A. [UNESP] |
dc.subject.por.fl_str_mv |
Best association Classification Colorectal cancer Feature selection Genetic algorithm Non-Hodgkin lymphoma |
topic |
Best association Classification Colorectal cancer Feature selection Genetic algorithm Non-Hodgkin lymphoma |
description |
The analysis of histological images is based on visual assessment of tissues by specialists using an optical microscopy. This task can be time-consuming and challenging, mainly due to the complexity of the structures and diseases under investigation. These facts have motivated the development of computational methods to support specialists in research and decision-making. Despite the different computational strategies available in the literature, the solutions based on genetic algorithm have not been fully explored to provide the best combination of features, selection algorithms and classifiers. In this paper, we describe an approach based on genetic algorithm able to evaluate a significant number of features, selection methods and classifiers in order to provide an acceptable association for the diagnosis and pattern recognition of non-Hodgkin lymphomas and colorectal cancer. The chromosomal structure was represented with four genes. The evaluation and selection of individuals, as well as the crossover and mutation processes, were defined to distinguish the groups under investigation, with the highest AUC value and the smallest number of features. The tests were performed considering 1512 features from histological images, different population sizes and number of iterations. An initial population of 50 individuals and 50 iterations provided the best result (AUC value of 0.984) for the colorectal histological images. For non-Hodgkin lymphoma images, the best result (AUC value of 0.947) was obtained with a population of 500 individuals and 50 iterations. The proposed methodology with detailed information regarding the methods, features and best associations are relevant contributions for the community interested in the study of pattern recognition of colorectal cancer and lymphomas. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-25T10:44:05Z 2021-06-25T10:44:05Z 2021-05-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/s10044-020-00931-3 Pattern Analysis and Applications, v. 24, n. 2, p. 483-496, 2021. 1433-755X 1433-7541 http://hdl.handle.net/11449/206801 10.1007/s10044-020-00931-3 2-s2.0-85095701837 |
url |
http://dx.doi.org/10.1007/s10044-020-00931-3 http://hdl.handle.net/11449/206801 |
identifier_str_mv |
Pattern Analysis and Applications, v. 24, n. 2, p. 483-496, 2021. 1433-755X 1433-7541 10.1007/s10044-020-00931-3 2-s2.0-85095701837 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Pattern Analysis and Applications |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
483-496 |
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_ |
1799964491406901248 |