Evaluation of an automatic dry eye test using MCDM methods and rank correlation
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://repositorio.inesctec.pt/handle/123456789/6628 http://dx.doi.org/10.1007/s11517-016-1534-5 |
Resumo: | Dry eye is an increasingly common disease in modern society which affects a wide range of population and has a negative impact on their daily activities, such as working with computers or driving. It can be diagnosed through an automatic clinical test for tear film lipid layer classification based on color and texture analysis. Up to now, researchers have mainly focused on the improvement of the image analysis step. However, there is still large room for improvement on the machine learning side. This paper presents a methodology to optimize this problem by means of class binarization, feature selection, and classification. The methodology can be used as a baseline in other classification problems to provide several solutions and evaluate their performance using a set of representative metrics and decision-making methods. When several decision-making methods are used, they may offer disagreeing rankings that will be solved by conflict handling in which rankings are merged into a single one. The experimental results prove the effectiveness of the proposed methodology in this domain. Also, its general purpose allows to adapt it to other classification problems in different fields such as medicine and biology. © 2016 International Federation for Medical and Biological Engineering |
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Evaluation of an automatic dry eye test using MCDM methods and rank correlationDry eye is an increasingly common disease in modern society which affects a wide range of population and has a negative impact on their daily activities, such as working with computers or driving. It can be diagnosed through an automatic clinical test for tear film lipid layer classification based on color and texture analysis. Up to now, researchers have mainly focused on the improvement of the image analysis step. However, there is still large room for improvement on the machine learning side. This paper presents a methodology to optimize this problem by means of class binarization, feature selection, and classification. The methodology can be used as a baseline in other classification problems to provide several solutions and evaluate their performance using a set of representative metrics and decision-making methods. When several decision-making methods are used, they may offer disagreeing rankings that will be solved by conflict handling in which rankings are merged into a single one. The experimental results prove the effectiveness of the proposed methodology in this domain. Also, its general purpose allows to adapt it to other classification problems in different fields such as medicine and biology. © 2016 International Federation for Medical and Biological Engineering2018-01-17T11:00:38Z2017-01-01T00:00:00Z2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/6628http://dx.doi.org/10.1007/s11517-016-1534-5engBarral,DiegoPeteiroBeatriz Remeseiro LópezMéndez,RebecaPenedo,ManuelG.info:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:54Zoai:repositorio.inesctec.pt:123456789/6628Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:46.929249Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Evaluation of an automatic dry eye test using MCDM methods and rank correlation |
title |
Evaluation of an automatic dry eye test using MCDM methods and rank correlation |
spellingShingle |
Evaluation of an automatic dry eye test using MCDM methods and rank correlation Barral,DiegoPeteiro |
title_short |
Evaluation of an automatic dry eye test using MCDM methods and rank correlation |
title_full |
Evaluation of an automatic dry eye test using MCDM methods and rank correlation |
title_fullStr |
Evaluation of an automatic dry eye test using MCDM methods and rank correlation |
title_full_unstemmed |
Evaluation of an automatic dry eye test using MCDM methods and rank correlation |
title_sort |
Evaluation of an automatic dry eye test using MCDM methods and rank correlation |
author |
Barral,DiegoPeteiro |
author_facet |
Barral,DiegoPeteiro Beatriz Remeseiro López Méndez,Rebeca Penedo,ManuelG. |
author_role |
author |
author2 |
Beatriz Remeseiro López Méndez,Rebeca Penedo,ManuelG. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Barral,DiegoPeteiro Beatriz Remeseiro López Méndez,Rebeca Penedo,ManuelG. |
description |
Dry eye is an increasingly common disease in modern society which affects a wide range of population and has a negative impact on their daily activities, such as working with computers or driving. It can be diagnosed through an automatic clinical test for tear film lipid layer classification based on color and texture analysis. Up to now, researchers have mainly focused on the improvement of the image analysis step. However, there is still large room for improvement on the machine learning side. This paper presents a methodology to optimize this problem by means of class binarization, feature selection, and classification. The methodology can be used as a baseline in other classification problems to provide several solutions and evaluate their performance using a set of representative metrics and decision-making methods. When several decision-making methods are used, they may offer disagreeing rankings that will be solved by conflict handling in which rankings are merged into a single one. The experimental results prove the effectiveness of the proposed methodology in this domain. Also, its general purpose allows to adapt it to other classification problems in different fields such as medicine and biology. © 2016 International Federation for Medical and Biological Engineering |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01T00:00:00Z 2017 2018-01-17T11:00:38Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.inesctec.pt/handle/123456789/6628 http://dx.doi.org/10.1007/s11517-016-1534-5 |
url |
http://repositorio.inesctec.pt/handle/123456789/6628 http://dx.doi.org/10.1007/s11517-016-1534-5 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/embargoedAccess |
eu_rights_str_mv |
embargoedAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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