Analysis of Features for Breast Cancer Recognition in Different Magnifications of Histopathological Images
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/IWSSIP48289.2020.9145129 http://hdl.handle.net/11449/221529 |
Resumo: | Breast cancer is one of the most common diseases in women in the world. There are various imaging techniques employed in the diagnosis. The histological image analysis supported by computational systems has proved to be quite effective in diagnosing the disease. In this paper, we present an approach to quantify and classify tissue samples of the breast based on features extracted from the intensity histogram, co-occurrence matrix and the Shannon, Renyi, Tsallis and Kapoor entropies. The attribute set was employed to obtain the feature vectors which were evaluated as inputs to the random forest and sequential minimal optimization algorithms with the 10-fold cross-validation technique. In this study, we investigated the proposed approach with images obtained in four levels of magnification of the publicly available Breast Cancer Histopathological Database. In the feature selection stage, we investigated the correlation-Based feature selection, ReliefF, information gain, gain ratio, one-R and symmetrical uncertainty algorithms for evaluating the performance of the proposed approach. The proposed approach achieved significant results of AUC and accuracy for all cases analyzed. The proposed approach obtained 0.997 for AUC and 97.6% for the accuracy metric. These results are considered relevant and this approach is useful as an automated protocol for the diagnosis of breast histological tissue. |
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Analysis of Features for Breast Cancer Recognition in Different Magnifications of Histopathological ImagesBreast cancerCADEntropyFeature ExtractionHistological ImageBreast cancer is one of the most common diseases in women in the world. There are various imaging techniques employed in the diagnosis. The histological image analysis supported by computational systems has proved to be quite effective in diagnosing the disease. In this paper, we present an approach to quantify and classify tissue samples of the breast based on features extracted from the intensity histogram, co-occurrence matrix and the Shannon, Renyi, Tsallis and Kapoor entropies. The attribute set was employed to obtain the feature vectors which were evaluated as inputs to the random forest and sequential minimal optimization algorithms with the 10-fold cross-validation technique. In this study, we investigated the proposed approach with images obtained in four levels of magnification of the publicly available Breast Cancer Histopathological Database. In the feature selection stage, we investigated the correlation-Based feature selection, ReliefF, information gain, gain ratio, one-R and symmetrical uncertainty algorithms for evaluating the performance of the proposed approach. The proposed approach achieved significant results of AUC and accuracy for all cases analyzed. The proposed approach obtained 0.997 for AUC and 97.6% for the accuracy metric. These results are considered relevant and this approach is useful as an automated protocol for the diagnosis of breast histological tissue.Faculty of Computer Science Federal University of UberlândiaFederal Institute of Triângulo MineiroSão Paulo State University Department of Computer Science and StatisticsSão Paulo State University Department of Computer Science and StatisticsUniversidade Federal de Uberlândia (UFU)Federal Institute of Triângulo MineiroUniversidade Estadual Paulista (UNESP)Carvalho, Rafael H. De O.Martins, Alessandro S.Neves, Leandro A. [UNESP]Do Nascimento, Marcelo Z.2022-04-28T19:29:14Z2022-04-28T19:29:14Z2020-07-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject39-44http://dx.doi.org/10.1109/IWSSIP48289.2020.9145129International Conference on Systems, Signals, and Image Processing, v. 2020-July, p. 39-44.2157-87022157-8672http://hdl.handle.net/11449/22152910.1109/IWSSIP48289.2020.91451292-s2.0-85089143109Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Conference on Systems, Signals, and Image Processinginfo:eu-repo/semantics/openAccess2022-04-28T19:29:14Zoai:repositorio.unesp.br:11449/221529Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-28T19:29:14Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Analysis of Features for Breast Cancer Recognition in Different Magnifications of Histopathological Images |
title |
Analysis of Features for Breast Cancer Recognition in Different Magnifications of Histopathological Images |
spellingShingle |
Analysis of Features for Breast Cancer Recognition in Different Magnifications of Histopathological Images Carvalho, Rafael H. De O. Breast cancer CAD Entropy Feature Extraction Histological Image |
title_short |
Analysis of Features for Breast Cancer Recognition in Different Magnifications of Histopathological Images |
title_full |
Analysis of Features for Breast Cancer Recognition in Different Magnifications of Histopathological Images |
title_fullStr |
Analysis of Features for Breast Cancer Recognition in Different Magnifications of Histopathological Images |
title_full_unstemmed |
Analysis of Features for Breast Cancer Recognition in Different Magnifications of Histopathological Images |
title_sort |
Analysis of Features for Breast Cancer Recognition in Different Magnifications of Histopathological Images |
author |
Carvalho, Rafael H. De O. |
author_facet |
Carvalho, Rafael H. De O. Martins, Alessandro S. Neves, Leandro A. [UNESP] Do Nascimento, Marcelo Z. |
author_role |
author |
author2 |
Martins, Alessandro S. Neves, Leandro A. [UNESP] Do Nascimento, Marcelo Z. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de Uberlândia (UFU) Federal Institute of Triângulo Mineiro Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Carvalho, Rafael H. De O. Martins, Alessandro S. Neves, Leandro A. [UNESP] Do Nascimento, Marcelo Z. |
dc.subject.por.fl_str_mv |
Breast cancer CAD Entropy Feature Extraction Histological Image |
topic |
Breast cancer CAD Entropy Feature Extraction Histological Image |
description |
Breast cancer is one of the most common diseases in women in the world. There are various imaging techniques employed in the diagnosis. The histological image analysis supported by computational systems has proved to be quite effective in diagnosing the disease. In this paper, we present an approach to quantify and classify tissue samples of the breast based on features extracted from the intensity histogram, co-occurrence matrix and the Shannon, Renyi, Tsallis and Kapoor entropies. The attribute set was employed to obtain the feature vectors which were evaluated as inputs to the random forest and sequential minimal optimization algorithms with the 10-fold cross-validation technique. In this study, we investigated the proposed approach with images obtained in four levels of magnification of the publicly available Breast Cancer Histopathological Database. In the feature selection stage, we investigated the correlation-Based feature selection, ReliefF, information gain, gain ratio, one-R and symmetrical uncertainty algorithms for evaluating the performance of the proposed approach. The proposed approach achieved significant results of AUC and accuracy for all cases analyzed. The proposed approach obtained 0.997 for AUC and 97.6% for the accuracy metric. These results are considered relevant and this approach is useful as an automated protocol for the diagnosis of breast histological tissue. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-07-01 2022-04-28T19:29:14Z 2022-04-28T19:29:14Z |
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/IWSSIP48289.2020.9145129 International Conference on Systems, Signals, and Image Processing, v. 2020-July, p. 39-44. 2157-8702 2157-8672 http://hdl.handle.net/11449/221529 10.1109/IWSSIP48289.2020.9145129 2-s2.0-85089143109 |
url |
http://dx.doi.org/10.1109/IWSSIP48289.2020.9145129 http://hdl.handle.net/11449/221529 |
identifier_str_mv |
International Conference on Systems, Signals, and Image Processing, v. 2020-July, p. 39-44. 2157-8702 2157-8672 10.1109/IWSSIP48289.2020.9145129 2-s2.0-85089143109 |
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.format.none.fl_str_mv |
39-44 |
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_ |
1799964630768943104 |