Title: A literature survey of automated detection of cervical cancer cell in Pap smear images

Authors: N. Lavanya Devi; P. Thirumurugan

Addresses: Department of Electronics and Communication Engineering, P.S.N.A. College of Engineering and Technology, Dindigul, Tamil Nadu, India ' Department of Electronics and Communication Engineering, P.S.N.A. College of Engineering and Technology, Dindigul, Tamil Nadu, India

Abstract: Cervical cancer is one of the major reasons for gynecologic cancer even though it can be treated if detected in the early stage. Pre-cancer identification of the uterine cervix can be done by the Pap smear test. In the present day scenario, Pap smear images are taken and the patient has to wait for an expert's suggestion. This time consuming conventional method can be replaced by automating the process, which gives a rapid and accurate result. The automated screening of cervical cancer saves human resources and material and gives better accuracy by reducing human errors than an expert's review. The major steps involved in the automated classification are preprocessing the image, segmentation, feature extraction, classification, and analysing the classification result. This paper discusses the various algorithms that were used for segmenting and classifying the abnormal and normal cells based on the features extracted.

Keywords: preprocessing; segmentation; classification; feature extraction; Pap smear test; cervical cancer; Pap smear images; gynecologic cancer; uterine cervix; abnormal cell; normal cell.

DOI: 10.1504/WRSTSD.2022.119330

World Review of Science, Technology and Sustainable Development, 2022 Vol.18 No.1, pp.74 - 82

Received: 06 Jun 2019
Accepted: 10 Sep 2020
Published online: 01 Dec 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article