Effective Features to Classify Skin Lesions in Dermoscopic images
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: | https://hdl.handle.net/10216/104033 |
Resumo: | Features such as shape and color are indispensable to determine whether a skin lesion is a melanoma or not. However, there are no fixed guidelines to define which features are effective and how to combine them for classification. This lack of definition impedes the development of the automatic analyses of dermoscopic images. In this work, a search for effective features was carried out using a support vector machine. Three image databases were used to verify the feasibility and sensitivity of the automatic classification used. The results showed which features had a major influence on the classification performance, and confirmed the need to use various types of features in this process. |
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Effective Features to Classify Skin Lesions in Dermoscopic imagesCiências Tecnológicas, Ciências médicas e da saúdeTechnological sciences, Medical and Health sciencesFeatures such as shape and color are indispensable to determine whether a skin lesion is a melanoma or not. However, there are no fixed guidelines to define which features are effective and how to combine them for classification. This lack of definition impedes the development of the automatic analyses of dermoscopic images. In this work, a search for effective features was carried out using a support vector machine. Three image databases were used to verify the feasibility and sensitivity of the automatic classification used. The results showed which features had a major influence on the classification performance, and confirmed the need to use various types of features in this process.2017-10-302017-10-30T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleimage/pngapplication/pdfhttps://hdl.handle.net/10216/104033eng0957-417410.1016/j.eswa.2017.05.003Zhen MaJoão Manuel R. S. Tavaresinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T15:32:18Zoai:repositorio-aberto.up.pt:10216/104033Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:26:00.917445Repositório 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 |
Effective Features to Classify Skin Lesions in Dermoscopic images |
title |
Effective Features to Classify Skin Lesions in Dermoscopic images |
spellingShingle |
Effective Features to Classify Skin Lesions in Dermoscopic images Zhen Ma Ciências Tecnológicas, Ciências médicas e da saúde Technological sciences, Medical and Health sciences |
title_short |
Effective Features to Classify Skin Lesions in Dermoscopic images |
title_full |
Effective Features to Classify Skin Lesions in Dermoscopic images |
title_fullStr |
Effective Features to Classify Skin Lesions in Dermoscopic images |
title_full_unstemmed |
Effective Features to Classify Skin Lesions in Dermoscopic images |
title_sort |
Effective Features to Classify Skin Lesions in Dermoscopic images |
author |
Zhen Ma |
author_facet |
Zhen Ma João Manuel R. S. Tavares |
author_role |
author |
author2 |
João Manuel R. S. Tavares |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Zhen Ma João Manuel R. S. Tavares |
dc.subject.por.fl_str_mv |
Ciências Tecnológicas, Ciências médicas e da saúde Technological sciences, Medical and Health sciences |
topic |
Ciências Tecnológicas, Ciências médicas e da saúde Technological sciences, Medical and Health sciences |
description |
Features such as shape and color are indispensable to determine whether a skin lesion is a melanoma or not. However, there are no fixed guidelines to define which features are effective and how to combine them for classification. This lack of definition impedes the development of the automatic analyses of dermoscopic images. In this work, a search for effective features was carried out using a support vector machine. Three image databases were used to verify the feasibility and sensitivity of the automatic classification used. The results showed which features had a major influence on the classification performance, and confirmed the need to use various types of features in this process. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-10-30 2017-10-30T00:00:00Z |
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 |
https://hdl.handle.net/10216/104033 |
url |
https://hdl.handle.net/10216/104033 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0957-4174 10.1016/j.eswa.2017.05.003 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
image/png 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 |
repository.mail.fl_str_mv |
|
_version_ |
1799136174122467328 |