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Pap Smear Images Classification Using Machine Learning:A Literature Matrix

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dc.contributor.authorShahrina Binti Ismailen_US
dc.contributor.authorNur Ain Aliasen_US
dc.contributor.authorWan Azani Mustafaen_US
dc.contributor.authorMohd Aminudin Jamlosen_US
dc.contributor.authorHiam Alquranen_US
dc.contributor.authorHafizul Fahri Hanafen_US
dc.contributor.authorKhairul Shakir Ab Rahmanen_US
dc.date.accessioned2024-05-29T01:58:44Z
dc.date.available2024-05-29T01:58:44Z
dc.date.issued2022
dc.date.submitted2023-2-9
dc.descriptionDiagnostics 2022, 12, 2900en_US
dc.description.abstractCervical 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 applicationsen_US
dc.identifier.citation: Alias, N.A.; Mustafa, W.A.; Jamlos, M.A.; Alquran, H.; Hanafi, H.F.; Ismail, S.; Rahman, K.S.A. Pap Smear Images Classification Using Machine Learning: A Literature Matrix. Diagnostics 2022, 12, 2900. https://doi.org/10.3390/ diagnostics12122900en_US
dc.identifier.doi10.3390/diagnostics12122900
dc.identifier.epage16
dc.identifier.issn2075-4418
dc.identifier.issue2900
dc.identifier.spage1
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/10019
dc.identifier.volume12
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofDiagnosticsen_US
dc.subjectcervical cancer; cell classification; review; SLRen_US
dc.titlePap Smear Images Classification Using Machine Learning:A Literature Matrixen_US
dc.typeArticleen_US
dspace.entity.typePublication

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