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Segmentation of cervical cells for automated screening of cervical cancer: a review

Segmentation of cervical cells for automated screening of cervical cancer: a review In automated screening of cervical cytology, the morphological features of cell play a determining role. To avoid false diagnosis, urgent need of precise extraction of these features led to emergence of new segmentation models. In this paper author aspire to present literature review of research done in the field of segmentation stage in automatic screening of cervical smear images. Total of 78 publications are considered for the time period of 40 years. A detailed study of segmentation technique proposed in each publication is considered, which presents a chronological development and up-gradation of segmentation models. This review assist researcher to have thorough knowledge of various state-of-art segmentation models and the problems and complexities required to be tackled, for unambiguous determination of malignancies in cervical cytology. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence Review Springer Journals

Segmentation of cervical cells for automated screening of cervical cancer: a review

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References (51)

Publisher
Springer Journals
Copyright
Copyright © Springer Nature B.V. 2019
Subject
Computer Science; Artificial Intelligence; Computer Science, general
ISSN
0269-2821
eISSN
1573-7462
DOI
10.1007/s10462-019-09735-2
Publisher site
See Article on Publisher Site

Abstract

In automated screening of cervical cytology, the morphological features of cell play a determining role. To avoid false diagnosis, urgent need of precise extraction of these features led to emergence of new segmentation models. In this paper author aspire to present literature review of research done in the field of segmentation stage in automatic screening of cervical smear images. Total of 78 publications are considered for the time period of 40 years. A detailed study of segmentation technique proposed in each publication is considered, which presents a chronological development and up-gradation of segmentation models. This review assist researcher to have thorough knowledge of various state-of-art segmentation models and the problems and complexities required to be tackled, for unambiguous determination of malignancies in cervical cytology.

Journal

Artificial Intelligence ReviewSpringer Journals

Published: Apr 12, 2020

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