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一种改进的残差网络宫颈癌细胞图像识别方法.docx

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一种改进的残差网络宫颈癌细胞图像识别方法 Title:AnImprovedResidualNetworkApproachforCervicalCancerCellImageRecognition Abstract: Cervicalcancerisoneoftheleadingcausesofcancer-relateddeathsinwomenworldwide.Earlyandaccuratedetectionofcervicalcancercellsisessentialforitssuccessfultreatment.Inrecentyears,deeplearningalgorithms,suchasresidualnetworks,haveshownremarkablesuccessinimagerecognitiontasks.Thispaperproposesanimprovedresidualnetworkapproachfortheaccuraterecognitionofcervicalcancercellsfrommicroscopicimages.Theproposedapproachaimstoenhancetheperformanceofexistingmethodsbyleveragingthepowerofresidualnetworksandincorporatingnovelarchitecturalimprovements.Experimentalresultsdemonstratethattheproposedmethodachievessuperiorperformanceintermsofaccuracy,sensitivity,andspecificitycomparedtoexistingstate-of-the-artmethods. 1.Introduction: Cervicalcancerisaglobalhealthconcern,particularlyindevelopingcountrieswithlimitedaccesstoscreeningandhealthcarefacilities.Earlydetectionofcervicalcancercellsplaysacrucialroleinimprovingtheprognosisandreducingmortalityrates.Imagerecognitiontechniqueshaveshownpromisingresultsinthefieldofcancerdetection,anddeeplearningmethodshaveemergedasthestate-of-the-artapproach.Thispaperpresentsanimprovedresidualnetworkapproachfortheaccuraterecognitionofcervicalcancercells. 2.RelatedWork: 2.1CervicalCancerCellImageRecognition: Thetraditionalapproachesforcervicalcancercellimagerecognitioninvolvedhand-craftedfeatureextractionfollowedbyclassification.However,thesemethodsheavilyrelyonmanualfeatureengineeringandmaynotcapturealltherelevantinformationpresentintheimages.Withtheadventofdeeplearning,convolutionalneuralnetworks(CNNs)haveshownexcellentperformanceinimagerecognitiontasks.Amongthem,residualnetworkshavegainedpopularityduetotheirabilitytotraindeepnetworkseffectivelybyusingskipconnections. 2.2ResidualNetworks: Residualnetworks(ResNets)areatypeofCNNsthatintroducedskipconnectionstofacilitatethetrainingofdeepnetworks.Theyallowthenetworktolearntheresidualmappinginsteadofdirectly