A Systematic Study of Challenges in Cervical Cytology Image Segmentation of Intelligence based Cervical Cancer Screening

Cervical Cancer (CC) is the fourth frequent dreadful cancers that prevails in women across the globe. Cervical cytology is being adopted as the standard technique for the screening of pre-cancerous stage and cancerous lesions detection. For accurate segmentation in cervical cytology, the smear images play a crucial role in any Artificial Intelligence based Computer Aided Diagnosis (CAD) method. This relies upon the precise segmentation of nucleus present in the cervical cytology smear images. The motive of this study deals with a systematic analysis of the various challenges that are encountered while segmenting the Cervical Cell Nucleus and the countermeasures that are incorporated to eliminate the misdiagnosis and missed diagnoses in the early stage of screening. With the motive of this, a systematic study of the published articles from the sources – Scopus, PubMed and Web of Science are analyzed and reviewed. The importance of this study is due to the intensity of the disease that still positions cervical cancer as a bothering disease that disrupts the life of about five lakh female population, every year across the world.