Classification of glomerular hypercellularity using convolutional features and support vector machine

Detalhes bibliográficos
Autor(a) principal: Chagas, Paulo
Data de Publicação: 2020
Outros Autores: Souza, Luiz, Araújo, Ikaro, Aldeman, Nayze, Duarte, Angelo, Angelo, Michele, dosSantos, Washington Luis Conrado, Oliveira, Luciano
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da FIOCRUZ (ARCA)
Texto Completo: https://www.arca.fiocruz.br/handle/icict/40666
Resumo: Brazilian National Health Council. To preserve confidentiality, the images (including those shown in the paper) were separated from other patient's data. No data presented herein allows patient identification. All the procedures were approved by the Ethics Committee for Research Involving Human subjects of the Gonçalo Moniz Institute from the Oswaldo Cruz Foundation (CPqGM/FIOCRUZ), Protocols No. 188/ 09 and No. 1817574.
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spelling Chagas, PauloSouza, LuizAraújo, IkaroAldeman, NayzeDuarte, AngeloAngelo, MicheledosSantos, Washington Luis ConradoOliveira, Luciano2020-04-07T14:11:39Z2020-04-07T14:11:39Z2020CHAGAS, Paulo et al. Classification of glomerular hypercellularity using convolutional features and support vector machine. Artificial Intelligence in Medicine, v. 103, 2020.0933-3657https://www.arca.fiocruz.br/handle/icict/4066610.1016/j.artmed.2020.101808Brazilian National Health Council. To preserve confidentiality, the images (including those shown in the paper) were separated from other patient's data. No data presented herein allows patient identification. All the procedures were approved by the Ethics Committee for Research Involving Human subjects of the Gonçalo Moniz Institute from the Oswaldo Cruz Foundation (CPqGM/FIOCRUZ), Protocols No. 188/ 09 and No. 1817574.Universidade Federal da Bahia. Salvador, BA, Brasil.Universidade Federal da Bahia. Salvador, BA, Brasil.Universidade Federal da Bahia. Salvador, BA, Brasil.Universidade Federal do Piauí. Departamento de Medicina Especializada. Terezina, PI, Brasil.Universidade Estadual de Feira de Santana. Salvador, BA, Brasil.Universidade Estadual de Feira de Santana. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil.Universidade Federal da Bahia. Salvador, BA, Brasil.Glomeruli are histological structures of the kidney cortex formed by interwoven blood capillaries, and are responsible for blood filtration. Glomerular lesions impair kidney filtration capability, leading to protein loss and metabolic waste retention. An example of lesion is the glomerular hypercellularity, which is characterized by an increase in the number of cell nuclei in different areas of the glomeruli. Glomerular hypercellularity is a frequent lesion present in different kidney diseases. Automatic detection of glomerular hypercellularity would accelerate the screening of scanned histological slides for the lesion, enhancing clinical diagnosis. Having this in mind, we propose a new approach for classification of hypercellularity in human kidney images. Our proposed method introduces a novel architecture of a convolutional neural network (CNN) along with a support vector machine, achieving near perfect average results on FIOCRUZ data set in a binary classification (lesion or normal). Additionally, classification of hypercellularity sub-lesions was also evaluated, considering mesangial, endocapilar and both lesions, reaching an average accuracy of 82%. Either in binary task or in the multi-classification one, our proposed method outperformed Xception, ResNet50 and InceptionV3 networks, as well as a traditional handcrafted-based method. To the best of our knowledge, this is the first study on deep learning over a data set of glomerular hypercellularity images of human kidney.engElsevierHipercelularidadeBiópsia renal humanaRede neural convolucionalHypercellularityHuman kidney biopsyConvolutional neural networkClassification of glomerular hypercellularity using convolutional features and support vector machineinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da FIOCRUZ (ARCA)instname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZLICENSElicense.txtlicense.txttext/plain; charset=utf-82991https://www.arca.fiocruz.br/bitstream/icict/40666/1/license.txt5a560609d32a3863062d77ff32785d58MD51ORIGINALChagas P Classification....pdfChagas P Classification....pdfapplication/pdf7803287https://www.arca.fiocruz.br/bitstream/icict/40666/2/Chagas%20P%20Classification....pdf8406d7cf2344ab16df8d3ee7ba290ed8MD52TEXTChagas P 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dc.title.pt_BR.fl_str_mv Classification of glomerular hypercellularity using convolutional features and support vector machine
title Classification of glomerular hypercellularity using convolutional features and support vector machine
spellingShingle Classification of glomerular hypercellularity using convolutional features and support vector machine
Chagas, Paulo
Hipercelularidade
Biópsia renal humana
Rede neural convolucional
Hypercellularity
Human kidney biopsy
Convolutional neural network
title_short Classification of glomerular hypercellularity using convolutional features and support vector machine
title_full Classification of glomerular hypercellularity using convolutional features and support vector machine
title_fullStr Classification of glomerular hypercellularity using convolutional features and support vector machine
title_full_unstemmed Classification of glomerular hypercellularity using convolutional features and support vector machine
title_sort Classification of glomerular hypercellularity using convolutional features and support vector machine
author Chagas, Paulo
author_facet Chagas, Paulo
Souza, Luiz
Araújo, Ikaro
Aldeman, Nayze
Duarte, Angelo
Angelo, Michele
dosSantos, Washington Luis Conrado
Oliveira, Luciano
author_role author
author2 Souza, Luiz
Araújo, Ikaro
Aldeman, Nayze
Duarte, Angelo
Angelo, Michele
dosSantos, Washington Luis Conrado
Oliveira, Luciano
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Chagas, Paulo
Souza, Luiz
Araújo, Ikaro
Aldeman, Nayze
Duarte, Angelo
Angelo, Michele
dosSantos, Washington Luis Conrado
Oliveira, Luciano
dc.subject.other.pt_BR.fl_str_mv Hipercelularidade
Biópsia renal humana
Rede neural convolucional
topic Hipercelularidade
Biópsia renal humana
Rede neural convolucional
Hypercellularity
Human kidney biopsy
Convolutional neural network
dc.subject.en.pt_BR.fl_str_mv Hypercellularity
Human kidney biopsy
Convolutional neural network
description Brazilian National Health Council. To preserve confidentiality, the images (including those shown in the paper) were separated from other patient's data. No data presented herein allows patient identification. All the procedures were approved by the Ethics Committee for Research Involving Human subjects of the Gonçalo Moniz Institute from the Oswaldo Cruz Foundation (CPqGM/FIOCRUZ), Protocols No. 188/ 09 and No. 1817574.
publishDate 2020
dc.date.accessioned.fl_str_mv 2020-04-07T14:11:39Z
dc.date.available.fl_str_mv 2020-04-07T14:11:39Z
dc.date.issued.fl_str_mv 2020
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.citation.fl_str_mv CHAGAS, Paulo et al. Classification of glomerular hypercellularity using convolutional features and support vector machine. Artificial Intelligence in Medicine, v. 103, 2020.
dc.identifier.uri.fl_str_mv https://www.arca.fiocruz.br/handle/icict/40666
dc.identifier.issn.pt_BR.fl_str_mv 0933-3657
dc.identifier.doi.none.fl_str_mv 10.1016/j.artmed.2020.101808
identifier_str_mv CHAGAS, Paulo et al. Classification of glomerular hypercellularity using convolutional features and support vector machine. Artificial Intelligence in Medicine, v. 103, 2020.
0933-3657
10.1016/j.artmed.2020.101808
url https://www.arca.fiocruz.br/handle/icict/40666
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositório Institucional da FIOCRUZ (ARCA)
instname:Fundação Oswaldo Cruz (FIOCRUZ)
instacron:FIOCRUZ
instname_str Fundação Oswaldo Cruz (FIOCRUZ)
instacron_str FIOCRUZ
institution FIOCRUZ
reponame_str Repositório Institucional da FIOCRUZ (ARCA)
collection Repositório Institucional da FIOCRUZ (ARCA)
bitstream.url.fl_str_mv https://www.arca.fiocruz.br/bitstream/icict/40666/1/license.txt
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