YOLOv8-Based Fracture Detection System

YOLOv8-Based Fracture Detection System | 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Article YOLOv8-Based Fracture Detection System Waleed Shaban, Wessam El-Behaidy, Wael badawy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5455810/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 Emergency departments in hospitals regularly contend with a substantial influx of patients suffering from bone fractures, with pediatric wrist trauma fractures representing a notable portion of these cases. Preceding surgical interventions, pediatric surgeons must gather information about the fracture's cause and analyze the fracture's condition through the interpretation of X-ray images. This process typically requires a blend of skills from radiologists and surgeons, involving extensive training. The rise of deep learning in the field of computer vision has highlighted the development of network models for fracture detection as a crucial area of research. This paper is centered on the utilization of the YOLOv8 algorithm to train models using the GRAZPEDWRI-DX bone fracture dataset after pre-processing with the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, which contains 6,091 pediatric patients X-ray images suffering from wrist trauma. The principal objective is to enhance the detection of fractures in these cases. The results of rigorous experimentation illuminate the merits of various YOLOv8 algorithm models, notably emphasizing the superiority of the YOLOv8m model with a 1024-pixel image size, which achieves an impressive mean average precision (mAP 50) of 0.743. Object Detection Fracture Detection CLAHE Pre-processing Pediatric Wrist Trauma Deep Learning YOLOv8 Algorithm X-ray images 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-5455810","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":378647335,"identity":"e02666ef-4a34-48b5-9b87-17f1b5b296cb","order_by":0,"name":"Waleed Shaban","email":"","orcid":"","institution":"Egyptian Russian University","correspondingAuthor":false,"prefix":"","firstName":"Waleed","middleName":"","lastName":"Shaban","suffix":""},{"id":378647336,"identity":"00327ec5-e120-4296-83a0-7720503151b8","order_by":1,"name":"Wessam El-Behaidy","email":"","orcid":"","institution":"Helwan University","correspondingAuthor":false,"prefix":"","firstName":"Wessam","middleName":"","lastName":"El-Behaidy","suffix":""},{"id":378647337,"identity":"c4392fee-c98f-4595-82bc-6316a09660e0","order_by":2,"name":"Wael badawy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBACgwMMjAc+ABl8EgwMEsRqYTg4A8hgI0nLYR7StBw/fOCwbZudHJt088MbP9sOM/C3dyfg1WJ2Ji3hcG5bsjGbzDFjy16gFokzZzfg13Igx+Bw7jbmxDaJBDMJ3m2HGQwkcgloOf/+w2HLbfX1bRLp3yT/EqPF/kYOw2HGbYcT2CRyzKSJssXyxjODg73/jhu2yZwptpb9l85D0C8G55MfPvhxplqeX7p94803Z6zl+Nt78WtBB808JCkHgTqSdYyCUTAKRsHwBwDzIE8O9tWPEgAAAABJRU5ErkJggg==","orcid":"","institution":"Egyptian Russian University","correspondingAuthor":true,"prefix":"","firstName":"Wael","middleName":"","lastName":"badawy","suffix":""}],"badges":[],"createdAt":"2024-11-14 18:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5455810/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5455810/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72574165,"identity":"72475b65-e0dd-46d3-8c33-deffd9c31344","added_by":"auto","created_at":"2024-12-30 03:46:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":794653,"visible":true,"origin":"","legend":"","description":"","filename":"BoneFractureFinalBlind.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5455810/v1_covered_89db6ac9-3753-4245-9979-a2019c036bac.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"YOLOv8-Based Fracture Detection System","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":"Object Detection, Fracture Detection, CLAHE Pre-processing, Pediatric Wrist Trauma, Deep Learning, YOLOv8 Algorithm, X-ray images","lastPublishedDoi":"10.21203/rs.3.rs-5455810/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5455810/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Emergency departments in hospitals regularly contend with a substantial influx of patients suffering from bone fractures, with pediatric wrist trauma fractures representing a notable portion of these cases. Preceding surgical interventions, pediatric surgeons must gather information about the fracture's cause and analyze the fracture's condition through the interpretation of X-ray images. This process typically requires a blend of skills from radiologists and surgeons, involving extensive training. The rise of deep learning in the field of computer vision has highlighted the development of network models for fracture detection as a crucial area of research. This paper is centered on the utilization of the YOLOv8 algorithm to train models using the GRAZPEDWRI-DX bone fracture dataset after pre-processing with the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, which contains 6,091 pediatric patients X-ray images suffering from wrist trauma. The principal objective is to enhance the detection of fractures in these cases. The results of rigorous experimentation illuminate the merits of various YOLOv8 algorithm models, notably emphasizing the superiority of the YOLOv8m model with a 1024-pixel image size, which achieves an impressive mean average precision (mAP 50) of 0.743. ","manuscriptTitle":"YOLOv8-Based Fracture Detection System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-13 16:43:47","doi":"10.21203/rs.3.rs-5455810/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":"66f6ef26-e021-466d-b3cc-61013a0e4f49","owner":[],"postedDate":"December 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T03:38:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-13 16:43:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5455810","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5455810","identity":"rs-5455810","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}