A New Pooling Method for Cnn-based Deep Learning Models

A New Pooling Method for Cnn-based Deep Learning Models | 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 A New Pooling Method for Cnn-based Deep Learning Models KEMAL TURHAN, Erşan Kalaycı, Sinem Özdemir This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5871802/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 Convolutional Neural Network (CNN) methods provide an effective architecture widely used in image classification tasks. The pooling method in CNN layers has a critical role in reducing the computational cost by preserving some information in the feature map. The primary objective of this study is to improve information loss in pooling methods used in the literature and enhance classification accuracy. The Turhan pooling method offers a weighting, balancing, and adjustment capability beyond traditional max-pooling and average-pooling methods. This method allows tuning the parameters of the two features with the highest signal that can generate action potentials in the pooling mechanism similar to biological neurons. The method enables to optimize pooling for specific datasets or tasks. The results demonstrate that the Turhan pooling method is effective and competes with different architectures such as CNN, AlexNet, U-Net, and ResNet-18 on the Cifar10 dataset, improving classification performance. Artificial intelligence Convolutional Neural Network Machine Learning Pooling Layer Turhan Pooling method Full Text Additional Declarations No competing interests reported. Supplementary Files suplementaryfile.docx 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-5871802","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":407089887,"identity":"8f7fe102-66ff-4948-b61a-d8b981b0e465","order_by":0,"name":"KEMAL TURHAN","email":"","orcid":"","institution":"Karadeniz Technical University","correspondingAuthor":false,"prefix":"","firstName":"KEMAL","middleName":"","lastName":"TURHAN","suffix":""},{"id":407089888,"identity":"18aaf9aa-fdde-4bc1-a99a-bf09d221ac6c","order_by":1,"name":"Erşan Kalaycı","email":"data:image/png;base64,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","orcid":"","institution":"Karadeniz Technical University","correspondingAuthor":true,"prefix":"","firstName":"Erşan","middleName":"","lastName":"Kalaycı","suffix":""},{"id":407089889,"identity":"edf64ecf-7a9e-41a8-84f0-0474887260d7","order_by":2,"name":"Sinem Özdemir","email":"","orcid":"","institution":"Karadeniz Technical University","correspondingAuthor":false,"prefix":"","firstName":"Sinem","middleName":"","lastName":"Özdemir","suffix":""}],"badges":[],"createdAt":"2025-01-21 08:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5871802/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5871802/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76004046,"identity":"0a5a0008-7bd9-4520-b2e8-ef9c0e9ed053","added_by":"auto","created_at":"2025-02-11 11:04:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":828995,"visible":true,"origin":"","legend":"","description":"","filename":"article.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5871802/v1_covered_c7e06e99-6687-4f2f-bd07-b8175a304483.pdf"},{"id":76002327,"identity":"e231149d-076b-4dfe-aaa0-d28e7ac5075d","added_by":"auto","created_at":"2025-02-11 10:40:39","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":35117,"visible":true,"origin":"","legend":"","description":"","filename":"suplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-5871802/v1/be5e9e6bc458c87d2545250e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eA New Pooling Method for Cnn-based Deep Learning Models\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Artificial intelligence, Convolutional Neural Network, Machine Learning, Pooling Layer, Turhan Pooling method","lastPublishedDoi":"10.21203/rs.3.rs-5871802/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5871802/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eConvolutional Neural Network (CNN) methods provide an effective architecture widely used in image classification tasks. 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