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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.
Artificial Intelligence Review – Springer Journals
Published: Apr 12, 2020
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