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A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification

Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal
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Int. J. Mol. Sci. 2019, 20(20), 5114; https://doi.org/10.3390/ijms20205114
Received: 20 September 2019 / Revised: 7 October 2019 / Accepted: 9 October 2019 / Published: 15 October 2019
(This article belongs to the Section Molecular Informatics)
Cervical cancer is the one of the most common cancers in women worldwide, affecting around 570,000 new patients each year. Although there have been great improvements over the years, current screening procedures can still suffer from long and tedious workflows and ambiguities. The increasing interest in the development of computer-aided solutions for cervical cancer screening is to aid with these common practical difficulties, which are especially frequent in the low-income countries where most deaths caused by cervical cancer occur. In this review, an overview of the disease and its current screening procedures is firstly introduced. Furthermore, an in-depth analysis of the most relevant computational methods available on the literature for cervical cells analysis is presented. Particularly, this work focuses on topics related to automated quality assessment, segmentation and classification, including an extensive literature review and respective critical discussion. Since the major goal of this timely review is to support the development of new automated tools that can facilitate cervical screening procedures, this work also provides some considerations regarding the next generation of computer-aided diagnosis systems and future research directions. View Full-Text
Keywords: cervical cancer; pap-smear; screening; machine learning; segmentation; classification; computer-aided diagnosis cervical cancer; pap-smear; screening; machine learning; segmentation; classification; computer-aided diagnosis
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MDPI and ACS Style

Conceição, T.; Braga, C.; Rosado, L.; Vasconcelos, M.J.M. A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification. Int. J. Mol. Sci. 2019, 20, 5114.

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