A Collaborative Hybrid network with Aggregated Attention for Pulmonary Nodule Detection

A Collaborative Hybrid network with Aggregated Attention for Pulmonary Nodule 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 A Collaborative Hybrid network with Aggregated Attention for Pulmonary Nodule Detection Jianle Chen, Jianyu Zhu, Lanhui Fu, Yuyan Lin, Huilian Liao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8819333/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 Lung cancer remains one of the most lethal malignancies worldwide, underscoring the critical importance of early detection for improving patient survival. Current imaging-based methods for pulmonary nodule detection, however, are often limited by low efficiency and suboptimal accuracy, falling short of the demands for precise clinical diagnosis. While deep learning has markedly advanced medical image analysis, contemporary detection models still exhibit notable shortcomings—including inadequate recognition capability, high rates of missed detection, and elevated false positives—particularly when dealing with small and morphologically complex nodules.To tackle these issues, this paper proposes a collaborative hybrid network with aggregated attention for pulmonary nodule detection, termed YOLOv8-RTA. The model integrates an RT-DETR-based Transformer decoder head into the YOLOv8 architecture, replacing its original detection head. This hybrid design mitigates the suppression of true bounding boxes caused by non-maximum suppression (NMS) and significantly reduces missed detections. Furthermore, an aggregated attention mechanism is introduced within the YOLOv8 backbone to expand the effective receptive field and enhance the discriminative power of deep features, thereby improving the representation and classification of tiny nodules.Experimental results on the Tianchi dataset demonstrate that YOLOv8-RTA achieves a mean average precision (mAP) of 94.98%, substantially outperforming several baseline models. Additional validation using real-world CT images from a hospital PACS system confirms the model’s robustness and stability in clinical settings, highlighting its strong potential for practical deployment in early lung cancer screening. pulmonary nodule detection YOLOv8 RT-DETR decoder head aggregated attention 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-8819333","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594065823,"identity":"c023975e-f758-4ffb-a8bb-61373e945b80","order_by":0,"name":"Jianle Chen","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine Shunde Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jianle","middleName":"","lastName":"Chen","suffix":""},{"id":594065824,"identity":"7cdcac25-3bec-4fbe-9a40-36d19dca74fe","order_by":1,"name":"Jianyu Zhu","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine Shunde Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jianyu","middleName":"","lastName":"Zhu","suffix":""},{"id":594065825,"identity":"b647e66c-cae3-49c6-b159-d0c049d0a7a9","order_by":2,"name":"Lanhui Fu","email":"","orcid":"","institution":"Wuyi University","correspondingAuthor":false,"prefix":"","firstName":"Lanhui","middleName":"","lastName":"Fu","suffix":""},{"id":594065826,"identity":"774e285f-750f-4f66-9856-0d309e51cb75","order_by":3,"name":"Yuyan Lin","email":"","orcid":"","institution":"Macau University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuyan","middleName":"","lastName":"Lin","suffix":""},{"id":594065827,"identity":"fec45763-f831-4bc1-b5f4-ef966c179af0","order_by":4,"name":"Huilian Liao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYBACPmYGBiCSkGFjYGB8ABOVwKeFDaqFB6iF2YA4LQxgLQw8IDZcJX4t7MwPHxe2WfDwsZ89Vvnlz2F53Qbmg7d5GOzycDuMzdh4ZhvQYTx5abdleA4bbjvAlmzNw5BcjMcvZtK8IC0MOWa3JSQOJ5gd4DGT5mE4kNiAUwv7N4gW/jdmxRIGIC383who4YHaIpFjxvghAWwLGyEtxcY850Ba3hhLMxxIN9x2mM3Yco5BMk4t/PzHNz7mKauTk+/PMfz444+1vNnx5oc33lTY4dSCAph5wCSIMMCvEg4YfxCpcBSMglEwCkYWAACrqELflpd9jgAAAABJRU5ErkJggg==","orcid":"","institution":"Guangzhou University of Chinese Medicine Shunde Hospital","correspondingAuthor":true,"prefix":"","firstName":"Huilian","middleName":"","lastName":"Liao","suffix":""}],"badges":[],"createdAt":"2026-02-08 05:38:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8819333/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8819333/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104790705,"identity":"524ef924-e8e7-4a2d-b397-e25a349633ca","added_by":"auto","created_at":"2026-03-17 08:34:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":676381,"visible":true,"origin":"","legend":"","description":"","filename":"ACollaborativeHybridnetworkwithAggregatedAttentionforPulmonaryNoduleDetectionJanuary2026.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8819333/v1_covered_a3b4f06d-9f97-4dce-9866-ebef7b9c3692.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Collaborative Hybrid network with Aggregated Attention for Pulmonary Nodule Detection","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":"pulmonary nodule detection, YOLOv8, RT-DETR decoder head, aggregated attention","lastPublishedDoi":"10.21203/rs.3.rs-8819333/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8819333/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Lung cancer remains one of the most lethal malignancies worldwide, underscoring the critical importance of early detection for improving patient survival. Current imaging-based methods for pulmonary nodule detection, however, are often limited by low efficiency and suboptimal accuracy, falling short of the demands for precise clinical diagnosis. While deep learning has markedly advanced medical image analysis, contemporary detection models still exhibit notable shortcomings—including inadequate recognition capability, high rates of missed detection, and elevated false positives—particularly when dealing with small and morphologically complex nodules.To tackle these issues, this paper proposes a collaborative hybrid network with aggregated attention for pulmonary nodule detection, termed YOLOv8-RTA. The model integrates an RT-DETR-based Transformer decoder head into the YOLOv8 architecture, replacing its original detection head. This hybrid design mitigates the suppression of true bounding boxes caused by non-maximum suppression (NMS) and significantly reduces missed detections. Furthermore, an aggregated attention mechanism is introduced within the YOLOv8 backbone to expand the effective receptive field and enhance the discriminative power of deep features, thereby improving the representation and classification of tiny nodules.Experimental results on the Tianchi dataset demonstrate that YOLOv8-RTA achieves a mean average precision (mAP) of 94.98%, substantially outperforming several baseline models. Additional validation using real-world CT images from a hospital PACS system confirms the model’s robustness and stability in clinical settings, highlighting its strong potential for practical deployment in early lung cancer screening. ","manuscriptTitle":"A Collaborative Hybrid network with Aggregated Attention for Pulmonary Nodule Detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-20 02:16:41","doi":"10.21203/rs.3.rs-8819333/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":"b3bf0994-bca2-44a3-83d8-aaab3edd61b4","owner":[],"postedDate":"February 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-17T08:28:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-20 02:16:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8819333","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8819333","identity":"rs-8819333","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}