Automated Cervical Cancer Image Analysis using Deep Learning Techniques from Pap-Smear Images: A Literature Review

Abstract

Cervical Cancer (CC) is the fourth most frequent disease among females. According to a 2020 report from WHO, approximately 6,04,127 females identified with cervical cancer worldwide and about 3,41,831 females estimated under the death cases from this cancer. The main purpose behind this paper aims to provide a review and comprehensive overview of the automatic segmentation and classification of DL algorithms, and also offers a description of their architectures in the cervical cancer image analysis from Pap smear images. The research articles are considered from publically available scholarly sites such as Google Scholar, IEEE, Science Direct, PubMed, Springer, and Elsevier on cervical cancer medical image analysis. Only those papers are included that are related to the detection of cervical cancer with DL algorithms. Each review articles are considered using three sets namely: Set1- Segmentation, classification, and cervical Cancer; Set2-Medical imaging, Machine learning, and pap-smear, and Set3- Pap Smear images, Automated System, and Classification. Most of the existing techniques achieve an accuracy of 97.89% on publicly available pap smear datasets (Herlev Dataset). In the conclusion, research gaps and limitations are mentioned that reduced the classification accuracy. For reducing the limitation, also discussed the recent advance and future perspective concerning cervical cancer detection.