Comparison of segmentation models used to identify mature and immature lymphocytes in blood film
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Revista de Engenharia e Pesquisa Aplicada |
Texto Completo: | http://revistas.poli.br/index.php/repa/article/view/2207 |
Resumo: | Acute Lymphocytic Leukemia (ALL) affects about 75 thousand people a year, among these 80% are children, being a highly invasive and fatal disease, rapid diagnosis is of great importance, traditional diagnostic methods are expensive and time-consuming, therefore, the use of image segmentation methods using artificial intelligence can help detect the elements of interest in blood slides, the lymphoblasts. This work compared the models: Segnet, Mobilenet Segnet, Vgg Segnet, Resnet50 Segnet, Vgg Unet, Resnet50 Unet, Mobilenet Unet, FCN 8, FCN 32 and FCN 32 Mobilenet, by pixel precision and execution time. The ALL-IDB database was used, containing blood slides from healthy patients and possible ALL. As a result, it was observed that MobileNet networks performed better, among them, Mobilenet Unet stood out, where the result of the mean average precision of the classes was 83.4%. |
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Comparison of segmentation models used to identify mature and immature lymphocytes in blood filmComparação de Modelos de Segmentação Utilizados na Identificação de Linfócitos Maduros e Imaturos Em Lâminas SanguíneasAcute Lymphocytic Leukemia (ALL) affects about 75 thousand people a year, among these 80% are children, being a highly invasive and fatal disease, rapid diagnosis is of great importance, traditional diagnostic methods are expensive and time-consuming, therefore, the use of image segmentation methods using artificial intelligence can help detect the elements of interest in blood slides, the lymphoblasts. This work compared the models: Segnet, Mobilenet Segnet, Vgg Segnet, Resnet50 Segnet, Vgg Unet, Resnet50 Unet, Mobilenet Unet, FCN 8, FCN 32 and FCN 32 Mobilenet, by pixel precision and execution time. The ALL-IDB database was used, containing blood slides from healthy patients and possible ALL. As a result, it was observed that MobileNet networks performed better, among them, Mobilenet Unet stood out, where the result of the mean average precision of the classes was 83.4%.A Leucemia Linfocítica Aguda (LLA) atinge cerca de 75 mil pessoas por ano, dentre estas 80% são crianças, sendo uma doença altamente invasiva e fatal o diagnóstico rápido é de grande importância, os métodos tradicionais de diagnóstico são caros e demorados, portanto, a utilização de métodos de segmentação de imagem utilizando inteligência artificial, podem auxiliar na detecção dos elementos de interesse em lâminas de sangue, os linfoblastos. Este trabalho comparou os modelos: Segnet, Mobilenet Segnet, Vgg Segnet, Resnet50 Segnet, Vgg Unet, Resnet50 Unet, Mobilenet Unet, FCN 8, FCN 32 e FCN 32 Mobilenet, pela precisão por pixel e tempo de execução. Utilizou-se a base de dados ALL-IDB, contendo lâminas de sangue de pacientes saudáveis e possíveis LLA. Como resultado, observou-se que as redes MobileNet desempenharam melhor, dentre elas, destacou-se a Mobilenet Unet onde o resultado da precisão média das classes foi de 83,4%.Escola Politécnica de Pernambuco2022-07-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionAvaliado pelos paresapplication/pdftext/htmlhttp://revistas.poli.br/index.php/repa/article/view/220710.25286/repa.v7i2.2207Journal of Engineering and Applied Research; Vol 7 No 2 (2022): Edição Especial em Inteligência Artificial; 12-22Revista de Engenharia e Pesquisa Aplicada; v. 7 n. 2 (2022): Edição Especial em Inteligência Artificial; 12-222525-425110.25286/repa.v7i2reponame:Revista de Engenharia e Pesquisa Aplicadainstname:Universidade Federal de Pernambuco (UFPE)instacron:UFPEporhttp://revistas.poli.br/index.php/repa/article/view/2207/813http://revistas.poli.br/index.php/repa/article/view/2207/814-Copyright (c) 2022 Thaise dos Santos Tenório, Avelino Alonso Junior, Matheus Marinho, Ana Clara Fragoso de Medeiros, Carmelo José Albanez Bastos Filhohttp://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessAlonso Junior, AvelinoMarinho, MatheusMedeiros, Ana Clara Fragoso deBastos Filho, Carmelo José Albanez2022-07-17T20:06:52Zoai:ojs.poli.br:article/2207Revistahttp://revistas.poli.br/index.php/repaONGhttp://revistas.poli.br/index.php/repa/oai||repa@poli.br2525-42512525-4251opendoar:2022-07-17T20:06:52Revista de Engenharia e Pesquisa Aplicada - Universidade Federal de Pernambuco (UFPE)false |
dc.title.none.fl_str_mv |
Comparison of segmentation models used to identify mature and immature lymphocytes in blood film Comparação de Modelos de Segmentação Utilizados na Identificação de Linfócitos Maduros e Imaturos Em Lâminas Sanguíneas |
title |
Comparison of segmentation models used to identify mature and immature lymphocytes in blood film |
spellingShingle |
Comparison of segmentation models used to identify mature and immature lymphocytes in blood film Alonso Junior, Avelino |
title_short |
Comparison of segmentation models used to identify mature and immature lymphocytes in blood film |
title_full |
Comparison of segmentation models used to identify mature and immature lymphocytes in blood film |
title_fullStr |
Comparison of segmentation models used to identify mature and immature lymphocytes in blood film |
title_full_unstemmed |
Comparison of segmentation models used to identify mature and immature lymphocytes in blood film |
title_sort |
Comparison of segmentation models used to identify mature and immature lymphocytes in blood film |
author |
Alonso Junior, Avelino |
author_facet |
Alonso Junior, Avelino Marinho, Matheus Medeiros, Ana Clara Fragoso de Bastos Filho, Carmelo José Albanez |
author_role |
author |
author2 |
Marinho, Matheus Medeiros, Ana Clara Fragoso de Bastos Filho, Carmelo José Albanez |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Alonso Junior, Avelino Marinho, Matheus Medeiros, Ana Clara Fragoso de Bastos Filho, Carmelo José Albanez |
description |
Acute Lymphocytic Leukemia (ALL) affects about 75 thousand people a year, among these 80% are children, being a highly invasive and fatal disease, rapid diagnosis is of great importance, traditional diagnostic methods are expensive and time-consuming, therefore, the use of image segmentation methods using artificial intelligence can help detect the elements of interest in blood slides, the lymphoblasts. This work compared the models: Segnet, Mobilenet Segnet, Vgg Segnet, Resnet50 Segnet, Vgg Unet, Resnet50 Unet, Mobilenet Unet, FCN 8, FCN 32 and FCN 32 Mobilenet, by pixel precision and execution time. The ALL-IDB database was used, containing blood slides from healthy patients and possible ALL. As a result, it was observed that MobileNet networks performed better, among them, Mobilenet Unet stood out, where the result of the mean average precision of the classes was 83.4%. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-07-15 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Avaliado pelos pares |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://revistas.poli.br/index.php/repa/article/view/2207 10.25286/repa.v7i2.2207 |
url |
http://revistas.poli.br/index.php/repa/article/view/2207 |
identifier_str_mv |
10.25286/repa.v7i2.2207 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
http://revistas.poli.br/index.php/repa/article/view/2207/813 http://revistas.poli.br/index.php/repa/article/view/2207/814 |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by-nc/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf text/html |
dc.coverage.none.fl_str_mv |
- |
dc.publisher.none.fl_str_mv |
Escola Politécnica de Pernambuco |
publisher.none.fl_str_mv |
Escola Politécnica de Pernambuco |
dc.source.none.fl_str_mv |
Journal of Engineering and Applied Research; Vol 7 No 2 (2022): Edição Especial em Inteligência Artificial; 12-22 Revista de Engenharia e Pesquisa Aplicada; v. 7 n. 2 (2022): Edição Especial em Inteligência Artificial; 12-22 2525-4251 10.25286/repa.v7i2 reponame:Revista de Engenharia e Pesquisa Aplicada instname:Universidade Federal de Pernambuco (UFPE) instacron:UFPE |
instname_str |
Universidade Federal de Pernambuco (UFPE) |
instacron_str |
UFPE |
institution |
UFPE |
reponame_str |
Revista de Engenharia e Pesquisa Aplicada |
collection |
Revista de Engenharia e Pesquisa Aplicada |
repository.name.fl_str_mv |
Revista de Engenharia e Pesquisa Aplicada - Universidade Federal de Pernambuco (UFPE) |
repository.mail.fl_str_mv |
||repa@poli.br |
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1798036000442679296 |