An automatic detection method for cervical liquid-based cells based on the improved YOLO V5s

An automatic detection method for cervical liquid-based cells based on the improved YOLO V5s | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article An automatic detection method for cervical liquid-based cells based on the improved YOLO V5s Xudong SHEN, TAO Yebo, WU Xianglian, Linfei CHEN, ShiTao SHEN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3776072/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract To address the insufficient local inference performance of existing object detection algorithms used in cervical liquid-based cytology, we propose an enhanced YOLOv5s network structure. This architecture dynamically adjusts the weights of channels and spatial attention modules, thereby improving the extraction of feature information from small objects and enhancing the network model's detection capabilities. Alongside this enhanced network model, we studied the Mixup data augmentation technique, which effectively increases the sample size and addresses data imbalance in the custom dataset. We employ CIoU as the loss function for bounding box regression, thereby improving the localization accuracy of the network model's bounding boxes. Comparative experiments on a self-compiled cervical liquid-based cytology dataset show that the improved algorithm achieves a mean Average Precision (mAP) of 0.921, a 5.6% point increase compared to the original YOLOv5s, thus validating the effectiveness of our approach. Physical sciences/Mathematics and computing/Computer science Health sciences/Diseases/Cancer YOLOv5s cervical liquid-based cells network models object detection data augmentation loss functions Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3776072","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":262904187,"identity":"3a76ea07-23b3-4e9d-a815-d8f1716bda5e","order_by":0,"name":"Xudong SHEN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDACCTDJDMIHDKBiBjhVo2lhSzCAqiZaC48BA1Fa5Gc3H3vM22YtZ86/5kMxb9sfeQb25m0SDDV3cGoxuHMs3XBmW7qx5Yy3G4AMA8MGnmNlEgzHnuHWIpFjJvGx7XDihhtnNxh8OGOQwAASYWw4jNthM/K/SSSCtZx5YJAA0iL/Br8Whhs5bBBbzvcwGHyoANnCg1+LwY00M8kZ59KNDW6wGRjOqDA2bONJK7ZIOIbPYcnPpHnKrOUMzh9+ZsxjICfPz354440PNXgcBgcSCWzg+GADEQlEaGBg4D/A/IAohaNgFIyCUTDiAACvRVI9BjtNVAAAAABJRU5ErkJggg==","orcid":"","institution":"Jiaxing Vocational and Technical College","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xudong","middleName":"","lastName":"SHEN","suffix":""},{"id":262904189,"identity":"85f92f59-5e85-4f9b-8279-a868844f97f8","order_by":1,"name":"TAO Yebo","email":"","orcid":"","institution":"Jiaxing Vocational and Technical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"TAO","middleName":"","lastName":"Yebo","suffix":""},{"id":262904190,"identity":"b78e0399-39df-4d2d-a1d4-0737d9c66db9","order_by":2,"name":"WU Xianglian","email":"","orcid":"","institution":"Jiaxing Vocational and Technical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"WU","middleName":"","lastName":"Xianglian","suffix":""},{"id":262904191,"identity":"b1cf40bc-364b-4fd3-8a52-b5b4d7b91562","order_by":3,"name":"Linfei CHEN","email":"","orcid":"","institution":"Jiaxing Jingzhu Biotechnology Co., Ltd","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Linfei","middleName":"","lastName":"CHEN","suffix":""},{"id":262904193,"identity":"82e41434-f6d4-4c5a-9b47-d4836936042d","order_by":4,"name":"ShiTao SHEN","email":"","orcid":"","institution":"Jiaxing Jingzhu Biotechnology Co., Ltd","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"ShiTao","middleName":"","lastName":"SHEN","suffix":""}],"badges":[],"createdAt":"2023-12-19 09:29:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3776072/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3776072/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49156166,"identity":"967741cf-571b-439e-85d9-dcf8682e5a17","added_by":"auto","created_at":"2024-01-04 03:52:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":604333,"visible":true,"origin":"","legend":"","description":"","filename":"AnautomaticdetectionmethodforcervicalliquidbasedcellsbasedontheimprovedYOLOV5s.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3776072/v1_covered_75343bab-a752-4d90-9d75-90b95ec60b51.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An automatic detection method for cervical liquid-based cells based on the improved YOLO V5s","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"YOLOv5s, cervical liquid-based cells, network models, object detection, data augmentation, loss functions","lastPublishedDoi":"10.21203/rs.3.rs-3776072/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3776072/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo address the insufficient local inference performance of existing object detection algorithms used in cervical liquid-based cytology, we propose an enhanced YOLOv5s network structure. This architecture dynamically adjusts the weights of channels and spatial attention modules, thereby improving the extraction of feature information from small objects and enhancing the network model's detection capabilities. Alongside this enhanced network model, we studied the Mixup data augmentation technique, which effectively increases the sample size and addresses data imbalance in the custom dataset. We employ CIoU as the loss function for bounding box regression, thereby improving the localization accuracy of the network model's bounding boxes. Comparative experiments on a self-compiled cervical liquid-based cytology dataset show that the improved algorithm achieves a mean Average Precision (mAP) of 0.921, a 5.6% point increase compared to the original YOLOv5s, thus validating the effectiveness of our approach.\u003c/p\u003e","manuscriptTitle":"An automatic detection method for cervical liquid-based cells based on the improved YOLO V5s","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-12-25 11:03:08","doi":"10.21203/rs.3.rs-3776072/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f6bb39e5-9038-4469-95e8-826639087b28","owner":[],"postedDate":"December 25th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":27743714,"name":"Physical sciences/Mathematics and computing/Computer science"},{"id":27743715,"name":"Health sciences/Diseases/Cancer"}],"tags":[],"updatedAt":"2024-01-04T03:44:15+00:00","versionOfRecord":[],"versionCreatedAt":"2023-12-25 11:03:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3776072","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3776072","identity":"rs-3776072","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}