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http://www.repositorio.ufop.br/jspui/handle/123456789/14486
Título: | A hierarchical feature-based methodology to perform cervical cancer classification. |
Autor(es): | Diniz, Débora Nasser Rezende, Mariana Trevisan Bianchi, Andrea Gomes Campos Carneiro, Cláudia Martins Ushizima, Daniela Mayumi Medeiros, Fátima Neusizeuma Sombra de Souza, Marcone Jamilson Freitas |
Palavras-chave: | Image classification Learning algorithm Random Forest classifier Hierarchical model Pap smear |
Data do documento: | 2021 |
Referência: | DINIZ, D. N. et al. A hierarchical feature-based methodology to perform cervical cancer classification. Applied Sciences-Basel, v. 11, n. 9, artigo 4091, 2021. Disponível em: <https://www.mdpi.com/2076-3417/11/9/4091>. Acesso em: 25 ago. 2021. |
Resumo: | Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells; accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visually by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods. |
URI: | http://www.repositorio.ufop.br/jspui/handle/123456789/14486 |
DOI: | https://doi.org/10.3390/app11094091 |
ISSN: | 2076-3417 |
Licença: | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Fonte: o PDF do artigo. |
Aparece nas coleções: | DECOM - Artigos publicados em periódicos |
Arquivos associados a este item:
Arquivo | Descrição | Tamanho | Formato | |
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ARTIGO_HierarchicalFeatureBased.pdf | 1,47 MB | Adobe PDF | Visualizar/Abrir |
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