Parkinson’s disease identification using restricted Boltzmann machines
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-319-64698-5_7 http://hdl.handle.net/11449/179134 |
Resumo: | Currently, Parkinson’s Disease (PD) has no cure or accurate diagnosis, reaching approximately 60,000 new cases yearly and worldwide, being more often in the elderly population. Its main symptoms can not be easily uncorrelated with other illness, being way more difficult to be identified at the early stages. As such, computer-aided tools have been recently used to assist in this task, but the challenge in the automatic identification of Parkinson’s Disease still persists. In order to cope with this problem, we propose to employ Restricted Boltzmann Machines (RBMs) to learn features in an unsupervised fashion by analyzing images from handwriting exams, which aim at assessing the writing skills of potential individuals. These are one of the main symptoms of PD-prone people, since such kind of ability ends up being severely affected. We show that RBMs can learn proper features that help supervised classifiers in the task of automatic identification of PD patients, as well as one can obtain a more compact representation of the exam for the sake of storage and computational load purposes. |
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Repositório Institucional da UNESP |
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Parkinson’s disease identification using restricted Boltzmann machinesMachine learningParkinson’s diseaseRestricted Boltzmann machinesCurrently, Parkinson’s Disease (PD) has no cure or accurate diagnosis, reaching approximately 60,000 new cases yearly and worldwide, being more often in the elderly population. Its main symptoms can not be easily uncorrelated with other illness, being way more difficult to be identified at the early stages. As such, computer-aided tools have been recently used to assist in this task, but the challenge in the automatic identification of Parkinson’s Disease still persists. In order to cope with this problem, we propose to employ Restricted Boltzmann Machines (RBMs) to learn features in an unsupervised fashion by analyzing images from handwriting exams, which aim at assessing the writing skills of potential individuals. These are one of the main symptoms of PD-prone people, since such kind of ability ends up being severely affected. We show that RBMs can learn proper features that help supervised classifiers in the task of automatic identification of PD patients, as well as one can obtain a more compact representation of the exam for the sake of storage and computational load purposes.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Department of Computing UFSCAR - Federal University of São CarlosEldorado Research InstituteBotucatu Medical School UNESP - São Paulo State UniversityOstbayerische Technische HochschuleSchool of Sciences UNESP - São Paulo State UniversityBotucatu Medical School UNESP - São Paulo State UniversitySchool of Sciences UNESP - São Paulo State UniversityFAPESP: #2014/12236-1FAPESP: #2014/16250-9FAPESP: #2015/25739-4FAPESP: #2016/21243-7CNPq: #306166/2014-3Universidade Federal de São Carlos (UFSCar)Eldorado Research InstituteUniversidade Estadual Paulista (Unesp)Ostbayerische Technische HochschulePereira, Clayton R.Passos, Leandro A.Lopes, Ricardo R.Weber, Silke A. T. [UNESP]Hook, ChristianPapa, João Paulo [UNESP]2018-12-11T17:33:53Z2018-12-11T17:33:53Z2017-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject70-80http://dx.doi.org/10.1007/978-3-319-64698-5_7Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10425 LNCS, p. 70-80.1611-33490302-9743http://hdl.handle.net/11449/17913410.1007/978-3-319-64698-5_72-s2.0-85028467802Scopusreponame: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)0,295info:eu-repo/semantics/openAccess2021-10-23T21:44:22Zoai:repositorio.unesp.br:11449/179134Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T21:44:22Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Parkinson’s disease identification using restricted Boltzmann machines |
title |
Parkinson’s disease identification using restricted Boltzmann machines |
spellingShingle |
Parkinson’s disease identification using restricted Boltzmann machines Pereira, Clayton R. Machine learning Parkinson’s disease Restricted Boltzmann machines |
title_short |
Parkinson’s disease identification using restricted Boltzmann machines |
title_full |
Parkinson’s disease identification using restricted Boltzmann machines |
title_fullStr |
Parkinson’s disease identification using restricted Boltzmann machines |
title_full_unstemmed |
Parkinson’s disease identification using restricted Boltzmann machines |
title_sort |
Parkinson’s disease identification using restricted Boltzmann machines |
author |
Pereira, Clayton R. |
author_facet |
Pereira, Clayton R. Passos, Leandro A. Lopes, Ricardo R. Weber, Silke A. T. [UNESP] Hook, Christian Papa, João Paulo [UNESP] |
author_role |
author |
author2 |
Passos, Leandro A. Lopes, Ricardo R. Weber, Silke A. T. [UNESP] Hook, Christian Papa, João Paulo [UNESP] |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Carlos (UFSCar) Eldorado Research Institute Universidade Estadual Paulista (Unesp) Ostbayerische Technische Hochschule |
dc.contributor.author.fl_str_mv |
Pereira, Clayton R. Passos, Leandro A. Lopes, Ricardo R. Weber, Silke A. T. [UNESP] Hook, Christian Papa, João Paulo [UNESP] |
dc.subject.por.fl_str_mv |
Machine learning Parkinson’s disease Restricted Boltzmann machines |
topic |
Machine learning Parkinson’s disease Restricted Boltzmann machines |
description |
Currently, Parkinson’s Disease (PD) has no cure or accurate diagnosis, reaching approximately 60,000 new cases yearly and worldwide, being more often in the elderly population. Its main symptoms can not be easily uncorrelated with other illness, being way more difficult to be identified at the early stages. As such, computer-aided tools have been recently used to assist in this task, but the challenge in the automatic identification of Parkinson’s Disease still persists. In order to cope with this problem, we propose to employ Restricted Boltzmann Machines (RBMs) to learn features in an unsupervised fashion by analyzing images from handwriting exams, which aim at assessing the writing skills of potential individuals. These are one of the main symptoms of PD-prone people, since such kind of ability ends up being severely affected. We show that RBMs can learn proper features that help supervised classifiers in the task of automatic identification of PD patients, as well as one can obtain a more compact representation of the exam for the sake of storage and computational load purposes. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01 2018-12-11T17:33:53Z 2018-12-11T17:33:53Z |
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/10.1007/978-3-319-64698-5_7 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10425 LNCS, p. 70-80. 1611-3349 0302-9743 http://hdl.handle.net/11449/179134 10.1007/978-3-319-64698-5_7 2-s2.0-85028467802 |
url |
http://dx.doi.org/10.1007/978-3-319-64698-5_7 http://hdl.handle.net/11449/179134 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10425 LNCS, p. 70-80. 1611-3349 0302-9743 10.1007/978-3-319-64698-5_7 2-s2.0-85028467802 |
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) 0,295 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
70-80 |
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_ |
1792961437979639808 |