Intestinal Parasites Classification Using Deep Belief Networks

Detalhes bibliográficos
Autor(a) principal: Roder, Mateus [UNESP]
Data de Publicação: 2020
Outros Autores: Passos, Leandro A. [UNESP], Ribeiro, Luiz Carlos Felix [UNESP], Benato, Barbara Caroline, Falcão, Alexandre Xavier, Papa, João Paulo [UNESP]
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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spelling 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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