Intestinal Parasites Classification Using Deep Belief Networks
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/10.1007/978-3-030-61401-0_23 http://hdl.handle.net/11449/233058 |
Resumo: | Currently, approximately 4 billion people are infected by intestinal parasites worldwide. Diseases caused by such infections constitute a public health problem in most tropical countries, leading to physical and mental disorders, and even death to children and immunodeficient individuals. Although subjected to high error rates, human visual inspection is still in charge of the vast majority of clinical diagnoses. In the past years, some works addressed intelligent computer-aided intestinal parasites classification, but they usually suffer from misclassification due to similarities between parasites and fecal impurities. In this paper, we introduce Deep Belief Networks to the context of automatic intestinal parasites classification. Experiments conducted over three datasets composed of eggs, larvae, and protozoa provided promising results, even considering unbalanced classes and also fecal impurities. |
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Repositório Institucional da UNESP |
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Intestinal Parasites Classification Using Deep Belief NetworksData augmentationDeep Belief NetworksIntestinal parasitesRestricted Boltzmann MachinesCurrently, approximately 4 billion people are infected by intestinal parasites worldwide. Diseases caused by such infections constitute a public health problem in most tropical countries, leading to physical and mental disorders, and even death to children and immunodeficient individuals. Although subjected to high error rates, human visual inspection is still in charge of the vast majority of clinical diagnoses. In the past years, some works addressed intelligent computer-aided intestinal parasites classification, but they usually suffer from misclassification due to similarities between parasites and fecal impurities. In this paper, we introduce Deep Belief Networks to the context of automatic intestinal parasites classification. Experiments conducted over three datasets composed of eggs, larvae, and protozoa provided promising results, even considering unbalanced classes and also fecal impurities.School of Sciences São Paulo State UniversityInstitute of Computing University of CampinasSchool of Sciences São Paulo State UniversityUniversidade Estadual Paulista (UNESP)Universidade Estadual de Campinas (UNICAMP)Roder, Mateus [UNESP]Passos, Leandro A. [UNESP]Ribeiro, Luiz Carlos Felix [UNESP]Benato, Barbara CarolineFalcão, Alexandre XavierPapa, João Paulo [UNESP]2022-05-01T01:25:44Z2022-05-01T01:25:44Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject242-251http://dx.doi.org/10.1007/978-3-030-61401-0_23Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 12415 LNAI, p. 242-251.1611-33490302-9743http://hdl.handle.net/11449/23305810.1007/978-3-030-61401-0_232-s2.0-85096520555Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccess2024-04-23T16:11:33Zoai:repositorio.unesp.br:11449/233058Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:28:14.774034Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Intestinal Parasites Classification Using Deep Belief Networks |
title |
Intestinal Parasites Classification Using Deep Belief Networks |
spellingShingle |
Intestinal Parasites Classification Using Deep Belief Networks Roder, Mateus [UNESP] Data augmentation Deep Belief Networks Intestinal parasites Restricted Boltzmann Machines |
title_short |
Intestinal Parasites Classification Using Deep Belief Networks |
title_full |
Intestinal Parasites Classification Using Deep Belief Networks |
title_fullStr |
Intestinal Parasites Classification Using Deep Belief Networks |
title_full_unstemmed |
Intestinal Parasites Classification Using Deep Belief Networks |
title_sort |
Intestinal Parasites Classification Using Deep Belief Networks |
author |
Roder, Mateus [UNESP] |
author_facet |
Roder, Mateus [UNESP] Passos, Leandro A. [UNESP] Ribeiro, Luiz Carlos Felix [UNESP] Benato, Barbara Caroline Falcão, Alexandre Xavier Papa, João Paulo [UNESP] |
author_role |
author |
author2 |
Passos, Leandro A. [UNESP] Ribeiro, Luiz Carlos Felix [UNESP] Benato, Barbara Caroline Falcão, Alexandre Xavier Papa, João Paulo [UNESP] |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Universidade Estadual de Campinas (UNICAMP) |
dc.contributor.author.fl_str_mv |
Roder, Mateus [UNESP] Passos, Leandro A. [UNESP] Ribeiro, Luiz Carlos Felix [UNESP] Benato, Barbara Caroline Falcão, Alexandre Xavier Papa, João Paulo [UNESP] |
dc.subject.por.fl_str_mv |
Data augmentation Deep Belief Networks Intestinal parasites Restricted Boltzmann Machines |
topic |
Data augmentation Deep Belief Networks Intestinal parasites Restricted Boltzmann Machines |
description |
Currently, approximately 4 billion people are infected by intestinal parasites worldwide. Diseases caused by such infections constitute a public health problem in most tropical countries, leading to physical and mental disorders, and even death to children and immunodeficient individuals. Although subjected to high error rates, human visual inspection is still in charge of the vast majority of clinical diagnoses. In the past years, some works addressed intelligent computer-aided intestinal parasites classification, but they usually suffer from misclassification due to similarities between parasites and fecal impurities. In this paper, we introduce Deep Belief Networks to the context of automatic intestinal parasites classification. Experiments conducted over three datasets composed of eggs, larvae, and protozoa provided promising results, even considering unbalanced classes and also fecal impurities. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01-01 2022-05-01T01:25:44Z 2022-05-01T01:25:44Z |
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/10.1007/978-3-030-61401-0_23 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 12415 LNAI, p. 242-251. 1611-3349 0302-9743 http://hdl.handle.net/11449/233058 10.1007/978-3-030-61401-0_23 2-s2.0-85096520555 |
url |
http://dx.doi.org/10.1007/978-3-030-61401-0_23 http://hdl.handle.net/11449/233058 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 12415 LNAI, p. 242-251. 1611-3349 0302-9743 10.1007/978-3-030-61401-0_23 2-s2.0-85096520555 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
242-251 |
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 |
|
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1808129323649990656 |