YOLOAttn-V8: Leveraging Attention Mechanism for Accurate Colorectal Polyp Detection

YOLOAttn-V8: Leveraging Attention Mechanism for Accurate Colorectal Polyp Detection | 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 YOLOAttn-V8: Leveraging Attention Mechanism for Accurate Colorectal Polyp Detection omid zare, mahdi Beigzadeh, Abel Abebe Bzuayene, Emrah Arslan, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6559498/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 Colorectal cancer has a high rate of mortality globally, often originating from polyps that, if detected early, can significantly improve patient outcomes. This study proposes an improved polyp detection method by incorporating a lightweight attention mechanism into the YOLOv8 framework, creating a model we call YOLOAttn-V8. Our methodology is crafted to address the complexities of polyp detection in endoscopic images, where discrepancies in size, form, and appearance hinder precise identification. By integrating both channel and spatial attention mechanisms, our model zeroes in on essential features while ensuring computational efficiency, rendering it apt for real-time clinical use. We trained and assessed our model on a comprehensive dataset that includes Kvasir-SEG, CVC-ClinicDB, ETIS, and CVC-ColonDB, yielding outstanding outcomes. On the ETIS dataset, our model achieved a recall of 93.9%, a precision of 97.6%, and an F1-score of 95.7%, whereas on CVC-ClinicDB, it reached a flawless recall of 100%, a precision of 99.3%, and an F1-score of 99.6%. These findings surpass many leading-edge methods, illustrating the capacity of our approach to improve early polyp detection and assist clinicians in alleviating the impact of colorectal cancer. Our research emphasizes the effectiveness of attention mechanisms in enhancing detection precision without significantly compromising speed, thereby paving the way for more dependable computer-aided diagnosis in endoscopy. Polyp detection Colorectal cancer YOLOv8 Attention mechanism Endoscopy 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-6559498","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452845136,"identity":"c1a64fff-d1aa-4feb-a2c3-faa6dde5bdea","order_by":0,"name":"omid 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