DHC-YOLO: Improved YOLOv8 for Lesion Detection in Brain Tumors, Colon Polyps, and Esophageal Cancer

DHC-YOLO: Improved YOLOv8 for Lesion Detection in Brain Tumors, Colon Polyps, and Esophageal Cancer | 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 DHC-YOLO: Improved YOLOv8 for Lesion Detection in Brain Tumors, Colon Polyps, and Esophageal Cancer Shaojie Ren, Jinmiao Song, Long Yu, Shengwei Tian, Jun Long This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4074263/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 The detection of lesions in various diseases remains a challenging task in medical image processing, given the diverse morphologies, sizes, and boundaries of lesions associated with different illnesses. In this paper, we propose an advanced lesion detection model named DHC-YOLO, which integrates Multi-Scale Dilated attention (MSDA) and multi-head self-attention (MHSA) within the YOLOv8 network. The method also introduces an enhanced feature fusion through the Concatenation (Concat) operation in the Feature Pyramid Networks (FPN) structure of YOLOv8. The DHC-YOLO model achieves superior performance in lesion detection by effectively aggregating semantic information across various scales in the attended receptive field, reducing redundancy in self-attention mechanisms without the need for complex operations or additional computational costs. The incorporation of MHSA enhances the network’s ability to extract diverse features, and the Concat operation in FPN improves multi-scale feature fusion. Our evaluations on brain tumor, colonic polyp, and esophageal cancer datasets demonstrate the superiority of our method over baseline YOLOv8 and several state-of-the-art object detection models. Specifically, on the brain tumor dataset, DHC-YOLO achieves mAP50 and mAP50:95 scores of 88.3% and 73.5%, respectively; on the colonic polyp dataset, the scores are 88.8% and 67.2%; and on the esophageal cancer dataset, the scores are 51.3% and 20.7%. These compelling results underscore the robust performance of DHC-YOLO in lesion detection tasks. Medical image analysis Lesion Detection YOLOv8 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-4074263","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":279085249,"identity":"f3975cbf-c077-49f5-8e49-9333fcb0a3da","order_by":0,"name":"Shaojie Ren","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Shaojie","middleName":"","lastName":"Ren","suffix":""},{"id":279085254,"identity":"5c7fc840-8451-44a2-bbf8-02d3fbba1fe2","order_by":1,"name":"Jinmiao Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYLACCQMGBjYGBsYHCRU1pGlhNnhw5hhplrFJPmxhJqxMvr338AuLgjvRfOyHj1UkNrAx8Ld3J+DVYnDmXJqFhMGz3DaetLQbiTtkGCTOnN2AX4tEjpmBhMHh3DaGHLMbiWfYgCK5+LXIz4Bp4X9jVpDYxkxYC8ONHOMHYC1A6xiI0mJw5owZA0TLs2SJhDPHeAj6Rb69x/izxJ/DufP7kw9+/FFRI8ff3kvAYcDokJZA4vEQUg4CzB8/EKNsFIyCUTAKRi4AAC+aR8CbPsQcAAAAAElFTkSuQmCC","orcid":"","institution":"Xinjiang University","correspondingAuthor":true,"prefix":"","firstName":"Jinmiao","middleName":"","lastName":"Song","suffix":""},{"id":279085255,"identity":"d0b8f8e5-95cf-4a32-b6e7-86869f6aea48","order_by":2,"name":"Long Yu","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Yu","suffix":""},{"id":279085256,"identity":"b0eca1f3-1d70-4358-8f01-b9f30c87a11d","order_by":3,"name":"Shengwei Tian","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Shengwei","middleName":"","lastName":"Tian","suffix":""},{"id":279085258,"identity":"6834c0f0-554e-4a6f-bc32-7c93ffec133f","order_by":4,"name":"Jun Long","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Long","suffix":""}],"badges":[],"createdAt":"2024-03-11 12:50:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4074263/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4074263/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53826432,"identity":"8ba3c3f4-31bb-4592-a232-fdbdeeaad7db","added_by":"auto","created_at":"2024-04-01 03:10:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":432258,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureLaTeXTemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4074263/v1_covered_6e8a7f1c-7fba-461d-ab8f-17198012a311.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DHC-YOLO: Improved YOLOv8 for Lesion Detection in Brain Tumors, Colon Polyps, and Esophageal Cancer","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":"Medical image analysis, Lesion Detection, YOLOv8, Attention Mechanism","lastPublishedDoi":"10.21203/rs.3.rs-4074263/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4074263/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe detection of lesions in various diseases remains a challenging task in medical image processing, given the diverse morphologies, sizes, and boundaries of lesions associated with different illnesses. In this paper, we propose an advanced lesion detection model named DHC-YOLO, which integrates Multi-Scale Dilated attention (MSDA) and multi-head self-attention (MHSA) within the YOLOv8 network. The method also introduces an enhanced feature fusion through the Concatenation (Concat) operation in the Feature Pyramid Networks (FPN) structure of YOLOv8. The DHC-YOLO model achieves superior performance in lesion detection by effectively aggregating semantic information across various scales in the attended receptive field, reducing redundancy in self-attention mechanisms without the need for complex operations or additional computational costs. The incorporation of MHSA enhances the network’s ability to extract diverse features, and the Concat operation in FPN improves multi-scale feature fusion. Our evaluations on brain tumor, colonic polyp, and esophageal cancer datasets demonstrate the superiority of our method over baseline YOLOv8 and several state-of-the-art object detection models. Specifically, on the brain tumor dataset, DHC-YOLO achieves mAP50 and mAP50:95 scores of 88.3% and 73.5%, respectively; on the colonic polyp dataset, the scores are 88.8% and 67.2%; and on the esophageal cancer dataset, the scores are 51.3% and 20.7%. These compelling results underscore the robust performance of DHC-YOLO in lesion detection tasks.\u003c/p\u003e","manuscriptTitle":"DHC-YOLO: Improved YOLOv8 for Lesion Detection in Brain Tumors, Colon Polyps, and Esophageal Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-15 04:26:58","doi":"10.21203/rs.3.rs-4074263/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":"6314ce6e-b3e9-4886-8ffd-7c9c32e35972","owner":[],"postedDate":"March 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-01T03:02:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-15 04:26:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4074263","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4074263","identity":"rs-4074263","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}