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Article

A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification

1
Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil
2
Departamento de Análises Clínicas, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil
3
Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
4
Departamento de Engenharia de Teleinformática, Universidade Federal do Ceará (UFC), Fortaleza 60020-181, Brazil
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Cecilia Di Ruberto
Appl. Sci. 2021, 11(9), 4091; https://doi.org/10.3390/app11094091
Received: 30 March 2021 / Revised: 24 April 2021 / Accepted: 26 April 2021 / Published: 30 April 2021
(This article belongs to the Special Issue Computer Aided Diagnosis)
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. View Full-Text
Keywords: image classification; learning algorithm; Random Forest classifier; hierarchical model; cervical lesions; cancer classification; feature extraction; Pap smear image classification; learning algorithm; Random Forest classifier; hierarchical model; cervical lesions; cancer classification; feature extraction; Pap smear
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MDPI and ACS Style

Diniz, D.N.; Rezende, M.T.; Bianchi, A.G.C.; Carneiro, C.M.; Ushizima, D.M.; de Medeiros, F.N.S.; Souza, M.J.F. A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification. Appl. Sci. 2021, 11, 4091. https://doi.org/10.3390/app11094091

AMA Style

Diniz DN, Rezende MT, Bianchi AGC, Carneiro CM, Ushizima DM, de Medeiros FNS, Souza MJF. A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification. Applied Sciences. 2021; 11(9):4091. https://doi.org/10.3390/app11094091

Chicago/Turabian Style

Diniz, Débora N., Mariana T. Rezende, Andrea G.C. Bianchi, Claudia M. Carneiro, Daniela M. Ushizima, Fátima N.S. de Medeiros, and Marcone J.F. Souza. 2021. "A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification" Applied Sciences 11, no. 9: 4091. https://doi.org/10.3390/app11094091

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