Parkinson disease identification using residual networks and optimum-path forest
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/SACI.2018.8441012 http://hdl.handle.net/11449/180187 |
Resumo: | Known as one of the most significant neurodegenerative diseases of the central nervous system, Parkinson's disease (PD) has a combination of several symptoms, such as tremor, postural instability, loss of movements, depression, anxiety, and dementia, among others. For the medicine, to point an exam that can diagnose a patient with such illness is challenging due to the symptoms that are easily related to other diseases. Therefore, developing computational methods capable of identifying PD in its early stages has been of paramount importance in the scientific community. Thence, this paper proposes to use a deep neural network called ResNet-50 to learn the patterns and extract features from images draw by patients. Afterwards, the Optimum-Path Forest (OPF) classifier is employed to identify Parkinson's disease automatically, being the results compared against two well-known classifiers, i.e., Support Vector Machines and the Bayes, as well as the ones provided by ResNet-50 itself. The experiments showed promising results concerning OPF, reachinz over 96% of identification rate. |
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Parkinson disease identification using residual networks and optimum-path forestMachine LearningParkinson's DiseaseResidual NetworksKnown as one of the most significant neurodegenerative diseases of the central nervous system, Parkinson's disease (PD) has a combination of several symptoms, such as tremor, postural instability, loss of movements, depression, anxiety, and dementia, among others. For the medicine, to point an exam that can diagnose a patient with such illness is challenging due to the symptoms that are easily related to other diseases. Therefore, developing computational methods capable of identifying PD in its early stages has been of paramount importance in the scientific community. Thence, this paper proposes to use a deep neural network called ResNet-50 to learn the patterns and extract features from images draw by patients. Afterwards, the Optimum-Path Forest (OPF) classifier is employed to identify Parkinson's disease automatically, being the results compared against two well-known classifiers, i.e., Support Vector Machines and the Bayes, as well as the ones provided by ResNet-50 itself. The experiments showed promising results concerning OPF, reachinz over 96% of identification rate.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)UFSCAR Federal University of São Carlos Department of ComputingUNESP São Paulo State University School of SciencesCTI Renato ArcherIFSP-Federal Institute of São Paulo Department of ComputingUNESP São Paulo State University Medical SchoolOstbayerische Tech. Hochschule Fakultät Informatik/MathematikUNESP São Paulo State University School of SciencesUNESP São Paulo State University Medical SchoolFAPESP: #2013/07375-0FAPESP: #2014/12236-1FAPESP: #2014/16250-9FAPESP: #2015/25739-4FAPESP: #2016/21243-7CNPq: #306166/2014-3CNPq: #307066/2017-7Universidade Federal de São Carlos (UFSCar)Universidade Estadual Paulista (Unesp)CTI Renato ArcherIFSP-Federal Institute of São PauloFakultät Informatik/MathematikPassos, Leandro A.Pereira, Clayton R. [UNESP]Rezende, Edmar R.S.Carvalho, Tiago J.Weber, Silke A.T. [UNESP]Hook, ChristianPapa, Joao P. [UNESP]2018-12-11T17:38:32Z2018-12-11T17:38:32Z2018-08-20info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject325-329http://dx.doi.org/10.1109/SACI.2018.8441012SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings, p. 325-329.http://hdl.handle.net/11449/18018710.1109/SACI.2018.84410122-s2.0-85053428839Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengSACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedingsinfo:eu-repo/semantics/openAccess2021-10-23T21:47:01Zoai:repositorio.unesp.br:11449/180187Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T21:47:01Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Parkinson disease identification using residual networks and optimum-path forest |
title |
Parkinson disease identification using residual networks and optimum-path forest |
spellingShingle |
Parkinson disease identification using residual networks and optimum-path forest Passos, Leandro A. Machine Learning Parkinson's Disease Residual Networks |
title_short |
Parkinson disease identification using residual networks and optimum-path forest |
title_full |
Parkinson disease identification using residual networks and optimum-path forest |
title_fullStr |
Parkinson disease identification using residual networks and optimum-path forest |
title_full_unstemmed |
Parkinson disease identification using residual networks and optimum-path forest |
title_sort |
Parkinson disease identification using residual networks and optimum-path forest |
author |
Passos, Leandro A. |
author_facet |
Passos, Leandro A. Pereira, Clayton R. [UNESP] Rezende, Edmar R.S. Carvalho, Tiago J. Weber, Silke A.T. [UNESP] Hook, Christian Papa, Joao P. [UNESP] |
author_role |
author |
author2 |
Pereira, Clayton R. [UNESP] Rezende, Edmar R.S. Carvalho, Tiago J. Weber, Silke A.T. [UNESP] Hook, Christian Papa, Joao P. [UNESP] |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Carlos (UFSCar) Universidade Estadual Paulista (Unesp) CTI Renato Archer IFSP-Federal Institute of São Paulo Fakultät Informatik/Mathematik |
dc.contributor.author.fl_str_mv |
Passos, Leandro A. Pereira, Clayton R. [UNESP] Rezende, Edmar R.S. Carvalho, Tiago J. Weber, Silke A.T. [UNESP] Hook, Christian Papa, Joao P. [UNESP] |
dc.subject.por.fl_str_mv |
Machine Learning Parkinson's Disease Residual Networks |
topic |
Machine Learning Parkinson's Disease Residual Networks |
description |
Known as one of the most significant neurodegenerative diseases of the central nervous system, Parkinson's disease (PD) has a combination of several symptoms, such as tremor, postural instability, loss of movements, depression, anxiety, and dementia, among others. For the medicine, to point an exam that can diagnose a patient with such illness is challenging due to the symptoms that are easily related to other diseases. Therefore, developing computational methods capable of identifying PD in its early stages has been of paramount importance in the scientific community. Thence, this paper proposes to use a deep neural network called ResNet-50 to learn the patterns and extract features from images draw by patients. Afterwards, the Optimum-Path Forest (OPF) classifier is employed to identify Parkinson's disease automatically, being the results compared against two well-known classifiers, i.e., Support Vector Machines and the Bayes, as well as the ones provided by ResNet-50 itself. The experiments showed promising results concerning OPF, reachinz over 96% of identification rate. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-11T17:38:32Z 2018-12-11T17:38:32Z 2018-08-20 |
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/10.1109/SACI.2018.8441012 SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings, p. 325-329. http://hdl.handle.net/11449/180187 10.1109/SACI.2018.8441012 2-s2.0-85053428839 |
url |
http://dx.doi.org/10.1109/SACI.2018.8441012 http://hdl.handle.net/11449/180187 |
identifier_str_mv |
SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings, p. 325-329. 10.1109/SACI.2018.8441012 2-s2.0-85053428839 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
325-329 |
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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1792961898249977856 |