Effective Features to Classify Skin Lesions in Dermoscopic images

Detalhes bibliográficos
Autor(a) principal: Zhen Ma
Data de Publicação: 2017
Outros Autores: João Manuel R. S. Tavares
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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spelling 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
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10.1016/j.eswa.2017.05.003
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