Skin Lesion Computational Diagnosis of Dermoscopic Images: Ensemble Models based on Input Feature Manipulation
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/10216/106154 |
Resumo: | Background and objectives: The number of deaths worldwide due to melanoma has risen in recent times, in part because melanoma is the most aggressive type of skin cancer. Computational systems have been developed to assist dermatologists in early diagnosis of skin cancer, or even to monitor skin lesions. However, there still remains a challenge to improve classifiers for the diagnosis of such skin lesions. The main objective of this article is to evaluate different ensemble classification models based on input feature manipulation to diagnose skin lesions. Methods: Input feature manipulation processes are based on feature subset selections from shape properties, colour variation and texture analysis to generate diversity for the ensemble models. Three subset selection models are presented here: (1) a subset selection model based on specific feature groups, (2) a correlation-based subset selection model, and (3) a subset selection model based on feature selection algorithms. Each ensemble classification model is generated using an optimum-path forest classifier and integrated with a majority voting strategy. The proposed models were applied on a set of 1104 dermoscopic images using a cross-validation procedure. Results: The best results were obtained by the first ensemble classification model that generates a feature subset ensemble based on specific feature groups. The skin lesion diagnosis computational system achieved 94.3% accuracy, 91.8% sensitivity and 96.7% specificity. Conclusions: The input feature manipulation process based on specific feature subsets generated the greatest diversity for the ensemble classification model with very promising results. |
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Skin Lesion Computational Diagnosis of Dermoscopic Images: Ensemble Models based on Input Feature ManipulationCiências Tecnológicas, Ciências da engenharia e tecnologiasTechnological sciences, Engineering and technologyBackground and objectives: The number of deaths worldwide due to melanoma has risen in recent times, in part because melanoma is the most aggressive type of skin cancer. Computational systems have been developed to assist dermatologists in early diagnosis of skin cancer, or even to monitor skin lesions. However, there still remains a challenge to improve classifiers for the diagnosis of such skin lesions. The main objective of this article is to evaluate different ensemble classification models based on input feature manipulation to diagnose skin lesions. Methods: Input feature manipulation processes are based on feature subset selections from shape properties, colour variation and texture analysis to generate diversity for the ensemble models. Three subset selection models are presented here: (1) a subset selection model based on specific feature groups, (2) a correlation-based subset selection model, and (3) a subset selection model based on feature selection algorithms. Each ensemble classification model is generated using an optimum-path forest classifier and integrated with a majority voting strategy. The proposed models were applied on a set of 1104 dermoscopic images using a cross-validation procedure. Results: The best results were obtained by the first ensemble classification model that generates a feature subset ensemble based on specific feature groups. The skin lesion diagnosis computational system achieved 94.3% accuracy, 91.8% sensitivity and 96.7% specificity. Conclusions: The input feature manipulation process based on specific feature subsets generated the greatest diversity for the ensemble classification model with very promising results.2017-102017-10-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleimage/pngapplication/pdfhttps://hdl.handle.net/10216/106154eng0169-260710.1016/j.cmpb.2017.07.009Roberta B. OliveiraAledir S. PereiraJoão 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:RCAAP2023-11-29T12:52:16Zoai:repositorio-aberto.up.pt:10216/106154Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:28:21.478611Repositório 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 |
Skin Lesion Computational Diagnosis of Dermoscopic Images: Ensemble Models based on Input Feature Manipulation |
title |
Skin Lesion Computational Diagnosis of Dermoscopic Images: Ensemble Models based on Input Feature Manipulation |
spellingShingle |
Skin Lesion Computational Diagnosis of Dermoscopic Images: Ensemble Models based on Input Feature Manipulation Roberta B. Oliveira Ciências Tecnológicas, Ciências da engenharia e tecnologias Technological sciences, Engineering and technology |
title_short |
Skin Lesion Computational Diagnosis of Dermoscopic Images: Ensemble Models based on Input Feature Manipulation |
title_full |
Skin Lesion Computational Diagnosis of Dermoscopic Images: Ensemble Models based on Input Feature Manipulation |
title_fullStr |
Skin Lesion Computational Diagnosis of Dermoscopic Images: Ensemble Models based on Input Feature Manipulation |
title_full_unstemmed |
Skin Lesion Computational Diagnosis of Dermoscopic Images: Ensemble Models based on Input Feature Manipulation |
title_sort |
Skin Lesion Computational Diagnosis of Dermoscopic Images: Ensemble Models based on Input Feature Manipulation |
author |
Roberta B. Oliveira |
author_facet |
Roberta B. Oliveira Aledir S. Pereira João Manuel R. S. Tavares |
author_role |
author |
author2 |
Aledir S. Pereira João Manuel R. S. Tavares |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Roberta B. Oliveira Aledir S. Pereira João Manuel R. S. Tavares |
dc.subject.por.fl_str_mv |
Ciências Tecnológicas, Ciências da engenharia e tecnologias Technological sciences, Engineering and technology |
topic |
Ciências Tecnológicas, Ciências da engenharia e tecnologias Technological sciences, Engineering and technology |
description |
Background and objectives: The number of deaths worldwide due to melanoma has risen in recent times, in part because melanoma is the most aggressive type of skin cancer. Computational systems have been developed to assist dermatologists in early diagnosis of skin cancer, or even to monitor skin lesions. However, there still remains a challenge to improve classifiers for the diagnosis of such skin lesions. The main objective of this article is to evaluate different ensemble classification models based on input feature manipulation to diagnose skin lesions. Methods: Input feature manipulation processes are based on feature subset selections from shape properties, colour variation and texture analysis to generate diversity for the ensemble models. Three subset selection models are presented here: (1) a subset selection model based on specific feature groups, (2) a correlation-based subset selection model, and (3) a subset selection model based on feature selection algorithms. Each ensemble classification model is generated using an optimum-path forest classifier and integrated with a majority voting strategy. The proposed models were applied on a set of 1104 dermoscopic images using a cross-validation procedure. Results: The best results were obtained by the first ensemble classification model that generates a feature subset ensemble based on specific feature groups. The skin lesion diagnosis computational system achieved 94.3% accuracy, 91.8% sensitivity and 96.7% specificity. Conclusions: The input feature manipulation process based on specific feature subsets generated the greatest diversity for the ensemble classification model with very promising results. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-10 2017-10-01T00:00:00Z |
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/10216/106154 |
url |
https://hdl.handle.net/10216/106154 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0169-2607 10.1016/j.cmpb.2017.07.009 |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799135590571048960 |