Pap Smear Images Classification Using Machine Learning:A Literature Matrix

Cervical cancer is regularly diagnosed in women all over the world. This cancer is the seventh most frequent cancer globally and the fourth most prevalent cancer among women. Automated
and higher accuracy of cervical cancer classification methods are needed for the early diagnosis of
cancer. In addition, this study has proved that routine Pap smears could enhance clinical outcomes
by facilitating the early diagnosis of cervical cancer. Liquid-based cytology (LBC)/Pap smears for
advanced cervical screening is a highly effective precancerous cell detection technology based on
cell image analysis, where cells are classed as normal or abnormal. Computer-aided systems in
medical imaging have benefited greatly from extraordinary developments in artificial intelligence
(AI) technology. However, resource and computational cost constraints prevent the widespread use
of AI-based automation-assisted cervical cancer screening systems. Hence, this paper reviewed the
related studies that have been done by previous researchers related to the automation of cervical
cancer classification based on machine learning. The objective of this study is to systematically
review and analyses the current research on the classification of the cervical using machine learning.
The literature that has been reviewed is indexed by Scopus and Web of Science. As a result, for
the published paper access until October 2022, this study assessed past approaches for cervical cell
classification based on machine learning applications