Two-stage White Blood Cells Detection Combined with Semi-supervised Classification

Two-stage White Blood Cells Detection Combined with Semi-supervised Classification | 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 Research Article Two-stage White Blood Cells Detection Combined with Semi-supervised Classification HUIHUI SONG, ZHENG WANG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5422460/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 The classification and statistics of white blood cells (WBCs) are critical steps in the microscopic examination of blood smears. Traditional manual microscopy methods are time-consuming and labor-intensive, while machine learning-based automated detection approaches require a substantial amount of labeled data for model training, leading to high costs. To address this issue, this paper proposes a two-stage semi-supervised deep learning method for WBC detection. In the first stage, a region proposal network (RPN) with ResNet50 as the backbone is employed for the localization and segmentation of white blood cell images. In the second stage, a semi-supervised learning framework is utilized to train the WBC classifier. The model is trained and tested using 1,510 labeled blood cell microscopy images with WBC localization boxes. The proposed semi-supervised model achieves a classification accuracy of 86%, which is 3.2% higher than that of the fully supervised model. Furthermore, this two-stage model is compared with two end-to-end models, FasterRCNN and RetinaNet. The results demonstrate that the proposed two-stage model achieves an accuracy of 83.7% and a recall of 85.1% in detection tasks, both exceeding those of the FasterRCNN and RetinaNet models. Compared to a one-stage WBC detection model, the two-stage detection method allows for more thorough training of the WBC classifier, thereby enhancing overall detection performance. white blood cell two-stage detection medical imaging semi-supervision 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. 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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-5422460","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":389190528,"identity":"a640da82-4f20-4e10-a969-9c91dd72eddd","order_by":0,"name":"HUIHUI SONG","email":"","orcid":"","institution":"Zhongda Hospital, Southeast University","correspondingAuthor":false,"prefix":"","firstName":"HUIHUI","middleName":"","lastName":"SONG","suffix":""},{"id":389190529,"identity":"c7c86602-98a0-4b3e-b374-383e5d58e72c","order_by":1,"name":"ZHENG WANG","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYBAC9mY2hgMfIGwD4rTwHGZjODiDNC0H2BiYeUjTws6WeNjmj01iA3vzNgmGmjtEaGFmO3A4ty0tsYHnWJkEw7FnhLXYM7M3HM5tOJzYIJFjJsHYcJgYW4BaLP78T2yQf0O0FqDDGNgOAG3hIV5LwsHetmTjNp60YouEY8Ro4T9m/OHHHzvZfvbDG298qCFCCxywgYgEEjSMglEwCkbBKMADABMaNWdW2jleAAAAAElFTkSuQmCC","orcid":"","institution":"Southeast University","correspondingAuthor":true,"prefix":"","firstName":"ZHENG","middleName":"","lastName":"WANG","suffix":""}],"badges":[],"createdAt":"2024-11-09 14:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5422460/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5422460/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71815521,"identity":"86f2887d-b0fc-4171-9bb9-421261786f44","added_by":"auto","created_at":"2024-12-18 20:16:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":515401,"visible":true,"origin":"","legend":"","description":"","filename":"TwostageWhiteBloodCellsDetectioncombinedwithSemisupervisedClassification.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5422460/v1_covered_dab4504e-0443-4090-81ab-391834550c62.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Two-stage White Blood Cells Detection Combined with Semi-supervised Classification","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":"white blood cell, two-stage detection, medical imaging, semi-supervision","lastPublishedDoi":"10.21203/rs.3.rs-5422460/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5422460/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe classification and statistics of white blood cells (WBCs) are critical steps in the microscopic examination of blood smears. 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