Convolutional neural networks applied for Parkinson’s disease identification
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , |
Tipo de documento: | Capítulo de livro |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-319-50478-0_19 http://hdl.handle.net/11449/234483 |
Resumo: | Parkinson’s Disease (PD) is a chronic and progressive illness that affects hundreds of thousands of people worldwide. Although it is quite easy to identify someone affected by PD when the illness shows itself (e.g. tremors, slowness of movement and freezing-of-gait), most works have focused on studying the working mechanism of the disease in its very early stages. In such cases, drugs can be administered in order to increase the quality of life of the patients. Since the beginning, it is well-known that PD patients feature the micrography, which is related to muscle rigidity and tremors. As such, most exams to detect Parkinson’s Disease make use of handwritten assessment tools, where the individual is asked to perform some predefined tasks, such as drawing spirals and meanders on a template paper. Later, an expert analyses the drawings in order to classify the progressive of the disease. In this work, we are interested into aiding physicians in such task by means of machine learning techniques, which can learn proper information from digitized versions of the exams, and them recommending a probability of a given individual being affected by PD depending on its handwritten skills. Particularly, we are interested in deep learning techniques (i.e. Convolutional Neural Networks) due to their ability into learning features without human interaction. Additionally, we propose to fine-tune hyper-arameters of such techniques by means of meta-heuristic-based techniques, such as Bat Algorithm, Firefly Algorithm and Particle Swarm Optimization. |
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Convolutional neural networks applied for Parkinson’s disease identificationConvolutional Neural NetworksMachine learningMeta-heuristicsParkinson’s DiseaseParkinson’s Disease (PD) is a chronic and progressive illness that affects hundreds of thousands of people worldwide. Although it is quite easy to identify someone affected by PD when the illness shows itself (e.g. tremors, slowness of movement and freezing-of-gait), most works have focused on studying the working mechanism of the disease in its very early stages. In such cases, drugs can be administered in order to increase the quality of life of the patients. Since the beginning, it is well-known that PD patients feature the micrography, which is related to muscle rigidity and tremors. As such, most exams to detect Parkinson’s Disease make use of handwritten assessment tools, where the individual is asked to perform some predefined tasks, such as drawing spirals and meanders on a template paper. Later, an expert analyses the drawings in order to classify the progressive of the disease. In this work, we are interested into aiding physicians in such task by means of machine learning techniques, which can learn proper information from digitized versions of the exams, and them recommending a probability of a given individual being affected by PD depending on its handwritten skills. Particularly, we are interested in deep learning techniques (i.e. Convolutional Neural Networks) due to their ability into learning features without human interaction. Additionally, we propose to fine-tune hyper-arameters of such techniques by means of meta-heuristic-based techniques, such as Bat Algorithm, Firefly Algorithm and Particle Swarm Optimization.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Department of Computing Federal University of São CarlosDepartment of Computing São Paulo State UniversitySchool of Science and Technology Middlesex UniversityDepartment of Computing São Paulo State UniversityFAPESP: 2014/16250-9FAPESP: 2015/25739-4Universidade Federal de São Carlos (UFSCar)Universidade Estadual Paulista (UNESP)Middlesex UniversityPereira, Clayton R.Pereira, Danillo R. [UNESP]Papa, Joao P. [UNESP]Rosa, Gustavo H. [UNESP]Yang, Xin-She2022-05-02T18:17:47Z2022-05-02T18:17:47Z2016-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookPart377-390http://dx.doi.org/10.1007/978-3-319-50478-0_19Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9605 LNCS, p. 377-390.1611-33490302-9743http://hdl.handle.net/11449/23448310.1007/978-3-319-50478-0_192-s2.0-85006355972Scopusreponame: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/openAccess2022-05-02T18:17:48Zoai:repositorio.unesp.br:11449/234483Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-05-02T18:17:48Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Convolutional neural networks applied for Parkinson’s disease identification |
title |
Convolutional neural networks applied for Parkinson’s disease identification |
spellingShingle |
Convolutional neural networks applied for Parkinson’s disease identification Pereira, Clayton R. Convolutional Neural Networks Machine learning Meta-heuristics Parkinson’s Disease |
title_short |
Convolutional neural networks applied for Parkinson’s disease identification |
title_full |
Convolutional neural networks applied for Parkinson’s disease identification |
title_fullStr |
Convolutional neural networks applied for Parkinson’s disease identification |
title_full_unstemmed |
Convolutional neural networks applied for Parkinson’s disease identification |
title_sort |
Convolutional neural networks applied for Parkinson’s disease identification |
author |
Pereira, Clayton R. |
author_facet |
Pereira, Clayton R. Pereira, Danillo R. [UNESP] Papa, Joao P. [UNESP] Rosa, Gustavo H. [UNESP] Yang, Xin-She |
author_role |
author |
author2 |
Pereira, Danillo R. [UNESP] Papa, Joao P. [UNESP] Rosa, Gustavo H. [UNESP] Yang, Xin-She |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Carlos (UFSCar) Universidade Estadual Paulista (UNESP) Middlesex University |
dc.contributor.author.fl_str_mv |
Pereira, Clayton R. Pereira, Danillo R. [UNESP] Papa, Joao P. [UNESP] Rosa, Gustavo H. [UNESP] Yang, Xin-She |
dc.subject.por.fl_str_mv |
Convolutional Neural Networks Machine learning Meta-heuristics Parkinson’s Disease |
topic |
Convolutional Neural Networks Machine learning Meta-heuristics Parkinson’s Disease |
description |
Parkinson’s Disease (PD) is a chronic and progressive illness that affects hundreds of thousands of people worldwide. Although it is quite easy to identify someone affected by PD when the illness shows itself (e.g. tremors, slowness of movement and freezing-of-gait), most works have focused on studying the working mechanism of the disease in its very early stages. In such cases, drugs can be administered in order to increase the quality of life of the patients. Since the beginning, it is well-known that PD patients feature the micrography, which is related to muscle rigidity and tremors. As such, most exams to detect Parkinson’s Disease make use of handwritten assessment tools, where the individual is asked to perform some predefined tasks, such as drawing spirals and meanders on a template paper. Later, an expert analyses the drawings in order to classify the progressive of the disease. In this work, we are interested into aiding physicians in such task by means of machine learning techniques, which can learn proper information from digitized versions of the exams, and them recommending a probability of a given individual being affected by PD depending on its handwritten skills. Particularly, we are interested in deep learning techniques (i.e. Convolutional Neural Networks) due to their ability into learning features without human interaction. Additionally, we propose to fine-tune hyper-arameters of such techniques by means of meta-heuristic-based techniques, such as Bat Algorithm, Firefly Algorithm and Particle Swarm Optimization. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-01-01 2022-05-02T18:17:47Z 2022-05-02T18:17:47Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/bookPart |
format |
bookPart |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/978-3-319-50478-0_19 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9605 LNCS, p. 377-390. 1611-3349 0302-9743 http://hdl.handle.net/11449/234483 10.1007/978-3-319-50478-0_19 2-s2.0-85006355972 |
url |
http://dx.doi.org/10.1007/978-3-319-50478-0_19 http://hdl.handle.net/11449/234483 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9605 LNCS, p. 377-390. 1611-3349 0302-9743 10.1007/978-3-319-50478-0_19 2-s2.0-85006355972 |
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 |
377-390 |
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_ |
1792962045267673088 |