A hierarchical feature-based methodology to perform cervical cancer classification.

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dc.contributor.authorDiniz, Débora Nasser
dc.contributor.authorRezende, Mariana Trevisan
dc.contributor.authorBianchi, Andrea Gomes Campos
dc.contributor.authorCarneiro, Cláudia Martins
dc.contributor.authorUshizima, Daniela Mayumi
dc.contributor.authorMedeiros, Fátima Neusizeuma Sombra de
dc.contributor.authorSouza, Marcone Jamilson Freitas
dc.date.accessioned2022-02-15T14:42:38Z
dc.date.available2022-02-15T14:42:38Z
dc.date.issued2021pt_BR
dc.description.abstractPrevention 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.pt_BR
dc.identifier.citationDINIZ, 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.pt_BR
dc.identifier.doihttps://doi.org/10.3390/app11094091pt_BR
dc.identifier.issn2076-3417
dc.identifier.urihttp://www.repositorio.ufop.br/jspui/handle/123456789/14486
dc.language.isoen_USpt_BR
dc.rightsabertopt_BR
dc.rights.licenseThis 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.pt_BR
dc.subjectImage classificationpt_BR
dc.subjectLearning algorithmpt_BR
dc.subjectRandom Forest classifierpt_BR
dc.subjectHierarchical modelpt_BR
dc.subjectPap smearpt_BR
dc.titleA hierarchical feature-based methodology to perform cervical cancer classification.pt_BR
dc.typeArtigo publicado em periodicopt_BR
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