A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification
Open Access
- 9 July 2021
- journal article
- research article
- Published by MDPI AG in Journal of Imaging
- Vol. 7 (7), 111
- https://doi.org/10.3390/jimaging7070111
Abstract
In recent years, deep learning methods have outperformed previous state-of-the-art machine learning techniques for several problems, including image classification. Classifying cells in Pap smear images is very challenging, and it is still of paramount importance for cytopathologists. The Pap test is a cervical cancer prevention test that tracks preneoplastic changes in cervical epithelial cells. Carrying out this exam is important in that early detection. It is directly related to a greater chance of curing or reducing the number of deaths caused by the disease. The analysis of Pap smears is exhaustive and repetitive, as it is performed manually by cytopathologists. Therefore, a tool that assists cytopathologists is needed. This work considers 10 deep convolutional neural networks and proposes an ensemble of the three best architectures to classify cervical cancer upon cell nuclei and reduce the professionals’ workload. The dataset used in the experiments is available in the Center for Recognition and Inspection of Cells (CRIC) Searchable Image Database. Considering the metrics of precision, recall, F1-score, accuracy, and sensitivity, the proposed ensemble improves previous methods shown in the literature for two- and three-class classification. We also introduce the six-class classification outcome.Keywords
Funding Information
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Finance Code 001)
- Fundação de Amparo à Pesquisa do Estado de Minas Gerais (PPM/CEX/FAPEMIG/6-76-17, PPSUS-FAPEMIG/APQ-03740-17)
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (303266/2019-8, 06/2015 - 306396/2015-7, 06/2019 305895/2019-2, 401120/2013/9)
- Pró-Reitoria de Pesquisa e Inovação, Universidade Federal de Ouro Preto (23109.000929/2020-88, 23109.000928/2020-33, DE-AC02-05CH11231)
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