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has been cited by the following article:

• TITLE: A Critical Analysis of Machine Learning and Deep Learning Methods for Cervical Cancer Screening

  AUTHORS: Muhtasim  , Mahmudur Rahman, Jakir Khan, Abu Sale Mohammad Mostafizur Rahman, Redoanul Haque, Md. Sumon Ali

  KEYWORDS: Cervical Cancer, Neoplasms, Screening, Machine Learning Techniques, Deep Learning Techniques

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.12, December 29, 2023

  ABSTRACT: Cervical cancer is a serious public health issue worldwide, and early identification is crucial for better patient outcomes. Recent study has investigated how ML and DL approaches may be used to increase the accuracy of vagina tests. In this piece, we conducted a thorough review of 50 research studies that applied these techniques. Our investigation compared the outcomes to well-known screening techniques and concentrated on the datasets used and performance measurements reported. According to the research, convolutional neural networks and other deep learning approaches have potential for lowering false positives and boosting screening precision. Although several research used small sample sizes or constrained datasets, this raises questions about how applicable the findings are. This paper discusses the advantages and disadvantages of the articles that were chosen, as well as prospective topics for future research, to further the application of ml and dl in cervical cancer screening. The development of cervical cancer screening technologies that are more precise, accessible, and can lead to better public health outcomes is significantly affected by these findings.