Aprendizado de máquina aplicado ao auxílio do diagnóstico da doença de Parkinson
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
Tipo de documento: | Tese |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFSCAR |
Texto Completo: | https://repositorio.ufscar.br/handle/ufscar/9299 |
Resumo: | Currently, it is not a trivial task to point out a test that can diagnose accurately enough a patient with Parkinson’s Disease, as well as it is quit difficult to assess the level of the disease. Experts recommend the application of different types of tests, many of them based on signs and biomedical imaging, such as electroencephalogram, computed tomography and magnetic resonance to aid the detection of the disease process, since as the age ranges, symptoms such as fatigue and weakness can hide diagnosis. In order to provide a more effective clinical information to doctors aiming at diagnosis with greater confidence, methodologies to perform the fusion of different imaging modalities have become increasingly popular and promising. Recently, the use of forms containing some activities using a biometric pen with multi-sensors have been applied for the detection of Parkinson’s Disease by means of handwriting analysis. However, information derived from the scanned image of the form itself, and the one obtained by same pen have not been used together for this purpose. Thus, this proposal aims using pattern recognition techniques and image processing aimed at using the information from the form together with data from the pen. We believe a possible improvement in the medical diagnosis of Parkinson’s Disease can be archived. Another contribution of this proposal, is the design of a multimodal database to aid in the diagnosis of Parkinson’s Disease. |
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Pereira, Clayton ReginaldoPapa, João Paulohttp://lattes.cnpq.br/9039182932747194http://lattes.cnpq.br/90836977748708522f034467-7e2f-4eb5-aeb8-e1e1d998bf5c2018-01-25T16:41:48Z2018-01-25T16:41:48Z2017-07-26PEREIRA, Clayton Reginaldo. Aprendizado de máquina aplicado ao auxílio do diagnóstico da doença de Parkinson. 2017. Tese (Doutorado em Ciência da Computação) – Universidade Federal de São Carlos, São Carlos, 2017. Disponível em: https://repositorio.ufscar.br/handle/ufscar/9299.https://repositorio.ufscar.br/handle/ufscar/9299Currently, it is not a trivial task to point out a test that can diagnose accurately enough a patient with Parkinson’s Disease, as well as it is quit difficult to assess the level of the disease. Experts recommend the application of different types of tests, many of them based on signs and biomedical imaging, such as electroencephalogram, computed tomography and magnetic resonance to aid the detection of the disease process, since as the age ranges, symptoms such as fatigue and weakness can hide diagnosis. In order to provide a more effective clinical information to doctors aiming at diagnosis with greater confidence, methodologies to perform the fusion of different imaging modalities have become increasingly popular and promising. Recently, the use of forms containing some activities using a biometric pen with multi-sensors have been applied for the detection of Parkinson’s Disease by means of handwriting analysis. However, information derived from the scanned image of the form itself, and the one obtained by same pen have not been used together for this purpose. Thus, this proposal aims using pattern recognition techniques and image processing aimed at using the information from the form together with data from the pen. We believe a possible improvement in the medical diagnosis of Parkinson’s Disease can be archived. Another contribution of this proposal, is the design of a multimodal database to aid in the diagnosis of Parkinson’s Disease.Atualmente, não é uma tarefa trivial apontar um exame que possa diagnosticar com precisão suficiente um paciente com mal de Parkinson, tendo como ponto importante também, após a constatação da enfermidade, a análise do nível da mesma. Especialistas recomendam a aplicação de diferentes tipos de exames, muitos deles baseados em sinais e imagens biomédicas, tais como eletroencefalograma, tomografia computadorizada e ressonância magnética para auxiliar no processo de detecção da doença, já que a faixa etária elevada e sintomas como cansaço e fraqueza podem ocultar o diagnóstico. Com o intuito de prover informações mais eficazes propiciando aos médicos um diagnóstico com maior confiança, metodologias para realizar a fusão entre diferentes modalidades de imagens tem se tornado cada vez mais populares e promissoras. Recentemente, a utilização de formulários contendo algumas atividades utilizando como ferramenta para o seu preenchimento uma caneta biométrica com multi-sensores tem sido aplicada para detecção do mal de Parkinson, efetuando o registro adquirido para análise da escrita. Entretanto, as informações oriundas da própria imagem digitalizada do formulário, bem como as mesmas obtidas pela caneta, ainda não foram utilizadas em conjunto para este fim. Desta forma, a presente proposta de tese de doutorado objetiva a utilização de técnicas de reconhecimento de padrões e processamento de imagens visando utilizar as diferentes informações provenientes do preenchimento do formulário em conjunto com dados provenientes da caneta, visando uma possível melhora no processo de auxílio ao diagnóstico médico do mal de Parkinson. Uma outra contribuição do trabalho é a criação de uma base de dados multimodal para o auxílio ao diagnóstico do mal de Parkinson.Não recebi financiamentoengUniversidade Federal de São CarlosCâmpus São CarlosPrograma de Pós-Graduação em Ciência da Computação - PPGCCUFSCarAprendizado do computadorDiagnósticoParkinson, Doença deMachine learningDiagnosisParkinson's diseaseCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAOAprendizado de máquina aplicado ao auxílio do diagnóstico da doença de Parkinsoninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisOnline600600a26a6b97-f6e5-4bd7-9c5a-876ad8cf02fdinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFSCARinstname:Universidade Federal de São Carlos (UFSCAR)instacron:UFSCARORIGINALTeseCRP.pdfTeseCRP.pdfapplication/pdf16817329https://repositorio.ufscar.br/bitstream/ufscar/9299/1/TeseCRP.pdfcaeccc84696f23e07efee854d9bff6f5MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81957https://repositorio.ufscar.br/bitstream/ufscar/9299/2/license.txtae0398b6f8b235e40ad82cba6c50031dMD52TEXTTeseCRP.pdf.txtTeseCRP.pdf.txtExtracted texttext/plain263355https://repositorio.ufscar.br/bitstream/ufscar/9299/3/TeseCRP.pdf.txta7a933f4381599b5abbf2f636217fa74MD53THUMBNAILTeseCRP.pdf.jpgTeseCRP.pdf.jpgIM Thumbnailimage/jpeg10126https://repositorio.ufscar.br/bitstream/ufscar/9299/4/TeseCRP.pdf.jpg047b11cbc121c2b7cd2afdacbb5e3d22MD54ufscar/92992023-09-18 18:31:22.124oai:repositorio.ufscar.br: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Repositório InstitucionalPUBhttps://repositorio.ufscar.br/oai/requestopendoar:43222023-09-18T18:31:22Repositório Institucional da UFSCAR - Universidade Federal de São Carlos (UFSCAR)false |
dc.title.por.fl_str_mv |
Aprendizado de máquina aplicado ao auxílio do diagnóstico da doença de Parkinson |
title |
Aprendizado de máquina aplicado ao auxílio do diagnóstico da doença de Parkinson |
spellingShingle |
Aprendizado de máquina aplicado ao auxílio do diagnóstico da doença de Parkinson Pereira, Clayton Reginaldo Aprendizado do computador Diagnóstico Parkinson, Doença de Machine learning Diagnosis Parkinson's disease CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
title_short |
Aprendizado de máquina aplicado ao auxílio do diagnóstico da doença de Parkinson |
title_full |
Aprendizado de máquina aplicado ao auxílio do diagnóstico da doença de Parkinson |
title_fullStr |
Aprendizado de máquina aplicado ao auxílio do diagnóstico da doença de Parkinson |
title_full_unstemmed |
Aprendizado de máquina aplicado ao auxílio do diagnóstico da doença de Parkinson |
title_sort |
Aprendizado de máquina aplicado ao auxílio do diagnóstico da doença de Parkinson |
author |
Pereira, Clayton Reginaldo |
author_facet |
Pereira, Clayton Reginaldo |
author_role |
author |
dc.contributor.authorlattes.por.fl_str_mv |
http://lattes.cnpq.br/9083697774870852 |
dc.contributor.author.fl_str_mv |
Pereira, Clayton Reginaldo |
dc.contributor.advisor1.fl_str_mv |
Papa, João Paulo |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/9039182932747194 |
dc.contributor.authorID.fl_str_mv |
2f034467-7e2f-4eb5-aeb8-e1e1d998bf5c |
contributor_str_mv |
Papa, João Paulo |
dc.subject.por.fl_str_mv |
Aprendizado do computador Diagnóstico Parkinson, Doença de |
topic |
Aprendizado do computador Diagnóstico Parkinson, Doença de Machine learning Diagnosis Parkinson's disease CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
dc.subject.eng.fl_str_mv |
Machine learning Diagnosis Parkinson's disease |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
description |
Currently, it is not a trivial task to point out a test that can diagnose accurately enough a patient with Parkinson’s Disease, as well as it is quit difficult to assess the level of the disease. Experts recommend the application of different types of tests, many of them based on signs and biomedical imaging, such as electroencephalogram, computed tomography and magnetic resonance to aid the detection of the disease process, since as the age ranges, symptoms such as fatigue and weakness can hide diagnosis. In order to provide a more effective clinical information to doctors aiming at diagnosis with greater confidence, methodologies to perform the fusion of different imaging modalities have become increasingly popular and promising. Recently, the use of forms containing some activities using a biometric pen with multi-sensors have been applied for the detection of Parkinson’s Disease by means of handwriting analysis. However, information derived from the scanned image of the form itself, and the one obtained by same pen have not been used together for this purpose. Thus, this proposal aims using pattern recognition techniques and image processing aimed at using the information from the form together with data from the pen. We believe a possible improvement in the medical diagnosis of Parkinson’s Disease can be archived. Another contribution of this proposal, is the design of a multimodal database to aid in the diagnosis of Parkinson’s Disease. |
publishDate |
2017 |
dc.date.issued.fl_str_mv |
2017-07-26 |
dc.date.accessioned.fl_str_mv |
2018-01-25T16:41:48Z |
dc.date.available.fl_str_mv |
2018-01-25T16:41:48Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
PEREIRA, Clayton Reginaldo. Aprendizado de máquina aplicado ao auxílio do diagnóstico da doença de Parkinson. 2017. Tese (Doutorado em Ciência da Computação) – Universidade Federal de São Carlos, São Carlos, 2017. Disponível em: https://repositorio.ufscar.br/handle/ufscar/9299. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufscar.br/handle/ufscar/9299 |
identifier_str_mv |
PEREIRA, Clayton Reginaldo. Aprendizado de máquina aplicado ao auxílio do diagnóstico da doença de Parkinson. 2017. Tese (Doutorado em Ciência da Computação) – Universidade Federal de São Carlos, São Carlos, 2017. Disponível em: https://repositorio.ufscar.br/handle/ufscar/9299. |
url |
https://repositorio.ufscar.br/handle/ufscar/9299 |
dc.language.iso.fl_str_mv |
eng |
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eng |
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600 600 |
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a26a6b97-f6e5-4bd7-9c5a-876ad8cf02fd |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de São Carlos Câmpus São Carlos |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Ciência da Computação - PPGCC |
dc.publisher.initials.fl_str_mv |
UFSCar |
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Universidade Federal de São Carlos Câmpus São Carlos |
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