GWO-Based Fed-UNet-CNN Model for Leukocyte Classification Across Developmental Stages

GWO-Based Fed-UNet-CNN Model for Leukocyte Classification Across Developmental Stages | 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 GWO-Based Fed-UNet-CNN Model for Leukocyte Classification Across Developmental Stages Dilip Nallamasa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9183195/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 Accurate leukocyte classification plays a crucial role in supporting hematological analysis and disease monitoring. In this study, we propose a deep learning-based computational framework for leukocyte classification across developmental stages using publicly available datasets. The workflow incorporates preprocessing techniques, including Contrast Limited Adaptive Histogram Equalization (CLAHE), followed by data augmentation using a Generative Adversarial Network (GAN) to improve dataset diversity and robustness. A U-Net architecture is employed for precise segmentation of leukocyte regions, and a Convolutional Neural Network (CNN) is used for feature extraction and classification. Additionally, a federated learning approach is integrated to enable collaborative model training across decentralized datasets while preserving data privacy. The proposed GWO-based Fed-UNet-CNN model demonstrates strong performance, achieving an overall accuracy of 99.29% on benchmark datasets. These results indicate the potential of the proposed approach as a computational decision-support tool for leukocyte classification. However, further validation using real-world clinical data is required before deployment in clinical settings. Leukocyte classification Medical image analysis Deep learning Convolutional neural networks (CNN) U-Net Federated learning Image segmentation Blood cell analysis Computer vision Hematology Generative adversarial networks (GAN) Data augmentation Full Text Additional Declarations The authors declare no competing interests. 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-9183195","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609742066,"identity":"be2d6002-4d51-476c-9959-bee7e9c88248","order_by":0,"name":"Dilip Nallamasa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDCCw8wNB8CMAwcSDD4wMCQQoYURquXggQeFM4jScoCxAar34IPPPMRo4TvO2Hi44JdNYt+xw4mbbdvs8vjZGxg/fMzBrUUS6LDDM/vSEmeeOZZsnNuWXCzZc4BZcuY23FoMQFp4ew4nbrhxJg2ohRnISGBj5iVKy/33339bttUTqYXnB1ALMJCNGdsOE9YC8UtDmvFMoBbDnnPHE2f2HGzG6xe+84cPfy74YyPbB4rKH2XVif3szQc/fMSjBQSYGdugLEY2MNmAXz1IC8MfGPMPPnWjYBSMglEwUgEAVedotJJLaR8AAAAASUVORK5CYII=","orcid":"","institution":"University at Buffalo","correspondingAuthor":true,"prefix":"","firstName":"Dilip","middleName":"","lastName":"Nallamasa","suffix":""}],"badges":[],"createdAt":"2026-03-21 05:04:57","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9183195/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9183195/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105301973,"identity":"c4e2f2a1-4184-4189-9096-26d9106a6ed9","added_by":"auto","created_at":"2026-03-24 13:57:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1135603,"visible":true,"origin":"","legend":"","description":"","filename":"MANUSCRIPT.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9183195/v1_covered_f6c1deb3-256a-47c5-97b1-8b100b187e24.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eGWO-Based Fed-UNet-CNN Model for Leukocyte Classification Across Developmental Stages\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Jawaharlal Nehru Technological University, Hyderabad","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":"Leukocyte classification, Medical image analysis, Deep learning, Convolutional neural networks (CNN), U-Net, Federated learning, Image segmentation, Blood cell analysis, Computer vision, Hematology, Generative adversarial networks (GAN), Data augmentation","lastPublishedDoi":"10.21203/rs.3.rs-9183195/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9183195/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate leukocyte classification plays a crucial role in supporting hematological analysis and disease monitoring. In this study, we propose a deep learning-based computational framework for leukocyte classification across developmental stages using publicly available datasets. The workflow incorporates preprocessing techniques, including Contrast Limited Adaptive Histogram Equalization (CLAHE), followed by data augmentation using a Generative Adversarial Network (GAN) to improve dataset diversity and robustness.\u003c/p\u003e\n\u003cp\u003eA U-Net architecture is employed for precise segmentation of leukocyte regions, and a Convolutional Neural Network (CNN) is used for feature extraction and classification. Additionally, a federated learning approach is integrated to enable collaborative model training across decentralized datasets while preserving data privacy.\u003c/p\u003e\n\u003cp\u003eThe proposed GWO-based Fed-UNet-CNN model demonstrates strong performance, achieving an overall accuracy of 99.29% on benchmark datasets. These results indicate the potential of the proposed approach as a computational decision-support tool for leukocyte classification. However, further validation using real-world clinical data is required before deployment in clinical settings.\u003c/p\u003e","manuscriptTitle":"GWO-Based Fed-UNet-CNN Model for Leukocyte Classification Across Developmental Stages","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 13:55:38","doi":"10.21203/rs.3.rs-9183195/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":"0f93f2b3-92e3-40b4-8542-f35504c0fb78","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-24T13:55:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 13:55:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9183195","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9183195","identity":"rs-9183195","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}