Efficient Brain Tumor Detection Based on Channel Shuffling

Efficient Brain Tumor Detection Based on Channel Shuffling | 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 Efficient Brain Tumor Detection Based on Channel Shuffling Pei Li, Rong Zhang, Zhongjie Zhu, Lei Zhang, Yongqiang Bai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5127189/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 Brain tumor detection is crucial for early diagnosis and treatment planning, as it involves automatically identifying and localizing brain tumors. However, existing methods often lack accuracy in detecting highly heterogeneous brain tumors and struggle to balance detection speed. To alleviate these issues, a novel brain tumor detection method termed channel shuffling YOLO (CS-YOLO) has been proposed, which optimizes both accuracy and detection speed. First, a depthwise separable convolution with a channel shuffling RepVGG module is designed. This module combines efficient parameter computation with robust feature extraction. It extracts deep features from images, thereby enhancing both accuracy and speed of brain tumor detection. Second, to enhance the network's performance in perceiving complex brain tumor targets, a novel convolutional multi-head self-attention module is constructed. This module learns long-range dependencies at lower resolutions, thereby improving the model's capability to recognize highly heterogeneous brain tumors. Finally, a lightweight channel shuffling convolution is designed and used to construct a lightweight residual module. This module dramatically reduces the number of parameters and the computational complexity of the model by splitting and shuffling channels, thus improving the model's learning and generalization performance. Experimental results demonstrate that the proposed method surpasses YOLOv6-L, YOLOv7, YOLOv8-L, and the latest RCS-YOLO in terms of detection accuracy and speed on the Br35H dataset. Compared to the state-of-the-art methods, the proposed CS-YOLO significantly enhances brain tumor detection accuracy and speed. Specifically, network computation in GFLOPs is reduced by 41%, FPS is increased by 14%, and accuracy AP is improved by 0.8%, achieving advanced performance. Brain tumor detection Channel shuffling Lightweight convolution Multi-head self-attention mechanism 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-5127189","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":359588671,"identity":"72455cea-2298-41e9-b67e-99331dbc5117","order_by":0,"name":"Pei Li","email":"","orcid":"","institution":"Zhejiang Wanli University","correspondingAuthor":false,"prefix":"","firstName":"Pei","middleName":"","lastName":"Li","suffix":""},{"id":359588672,"identity":"d2e120c4-9de6-4a08-a23e-cf5047eb1d3f","order_by":1,"name":"Rong Zhang","email":"","orcid":"","institution":"Zhejiang Wanli University","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Zhang","suffix":""},{"id":359588673,"identity":"94df79a1-217b-49a8-b25e-96414892edf7","order_by":2,"name":"Zhongjie Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIie2PsQqCQBiAvQ6czv0XJV8hEaSpZ+kQbIkaXIKiBMFJaBXyJXqG4Fx8AxfbGwwabInuqNartqD7hp9v+D9+fk1TKH4REIOAENRwHX+V4MEzQfH75CE6fJQ4u4QdL/aw7+wyf0kWo5lmJWUjS1DBJp5NwENF5dekCiLNZlR6BcPUt4AATbjURoppDFNXmujPZJNyiYzbhifzszQhfNNsCYyFYCM+iCvy9wHC0OKhm0MYmQUraQqhm8sSJw+Yec3WDpd9e1ot6RaCppUlAmxkD+kR8d27dQHqupd8sq5QKBR/xx0700FoZeEojwAAAABJRU5ErkJggg==","orcid":"","institution":"Zhejiang Wanli University","correspondingAuthor":true,"prefix":"","firstName":"Zhongjie","middleName":"","lastName":"Zhu","suffix":""},{"id":359588674,"identity":"b0934676-ce03-4d32-8af4-af922e178bc6","order_by":3,"name":"Lei Zhang","email":"","orcid":"","institution":"Zhejiang Wanli University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhang","suffix":""},{"id":359588675,"identity":"ad485908-6035-4ae4-a613-bc2f0efa927d","order_by":4,"name":"Yongqiang Bai","email":"","orcid":"","institution":"Zhejiang Wanli University","correspondingAuthor":false,"prefix":"","firstName":"Yongqiang","middleName":"","lastName":"Bai","suffix":""}],"badges":[],"createdAt":"2024-09-21 07:10:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5127189/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5127189/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70296014,"identity":"6e225b45-a81d-496e-9343-239f2700aa5a","added_by":"auto","created_at":"2024-12-02 00:31:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5553933,"visible":true,"origin":"","legend":"","description":"","filename":"SNEfficientBrainTumorDetectionBasedonChannelShuffling.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5127189/v1_covered_46fa3555-0258-484a-9eff-41f6c6d5c210.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Efficient Brain Tumor Detection Based on Channel Shuffling","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":"Brain tumor detection, Channel shuffling, Lightweight convolution, Multi-head self-attention mechanism","lastPublishedDoi":"10.21203/rs.3.rs-5127189/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5127189/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBrain tumor detection is crucial for early diagnosis and treatment planning, as it involves automatically identifying and localizing brain tumors. However, existing methods often lack accuracy in detecting highly heterogeneous brain tumors and struggle to balance detection speed. To alleviate these issues, a novel brain tumor detection method termed channel shuffling YOLO (CS-YOLO) has been proposed, which optimizes both accuracy and detection speed. First, a depthwise separable convolution with a channel shuffling RepVGG module is designed. This module combines efficient parameter computation with robust feature extraction. It extracts deep features from images, thereby enhancing both accuracy and speed of brain tumor detection. Second, to enhance the network's performance in perceiving complex brain tumor targets, a novel convolutional multi-head self-attention module is constructed. This module learns long-range dependencies at lower resolutions, thereby improving the model's capability to recognize highly heterogeneous brain tumors. 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