Comparative Analysis of Yolov5 and Yolov8 Deep Learning Models in the Detection and Anatomical Classification of Mandibular Fractures

Comparative Analysis of Yolov5 and Yolov8 Deep Learning Models in the Detection and Anatomical Classification of Mandibular Fractures | 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 Comparative Analysis of Yolov5 and Yolov8 Deep Learning Models in the Detection and Anatomical Classification of Mandibular Fractures Yasemin Kılıç, Utku Nezih Yılmaz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7999065/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 Objective: This study aimed to compare the diagnostic performance of two deep learning-based object detection algorithms—YOLOv5 and YOLOv8—for the automatic detection and anatomical classification of mandibular fractures on panoramic radiographs. Methods: A total of 400 panoramic radiographs with confirmed mandibular fractures were collected from the clinical archives of Dicle University, Faculty of Dentistry. The dataset was expanded to 980 images using data augmentation techniques (rotation, contrast adjustment, and flipping). Images were annotated according to five anatomical regions (symphysis, body, angle, ramus, and condyle). YOLOv5 and YOLOv8 models were trained on 80% of the dataset, validated on 10%, and tested on 10%. Model performance was evaluated using precision, recall, F1-score, mean average precision (mAP), and intersection over union (IoU). Results: YOLOv8 achieved higher diagnostic accuracy compared to YOLOv5. The YOLOv8 model yielded 0.85 precision, 0.83 recall, 0.84 F1-score, 0.89 mAP, and 0.82 IoU, while YOLOv5 achieved 0.81, 0.78, 0.79, 0.84, and 0.77, respectively. Visual inspection of detection maps confirmed that YOLOv8 produced more stable bounding box predictions and better localization across all anatomical zones, particularly in the angle and condylar regions. Conclusion: Both models demonstrated high potential for assisting clinicians in detecting mandibular fractures on panoramic radiographs. YOLOv8 exhibited superior precision and generalization ability, suggesting that advanced deep learning architectures may improve diagnostic workflows in dental trauma assessment. Mandibular fracture panoramic radiography deep learning YOLOv5 YOLOv8 artificial intelligence 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. 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Fractures\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Mandibular fracture, panoramic radiography, deep learning, YOLOv5, YOLOv8, artificial intelligence","lastPublishedDoi":"10.21203/rs.3.rs-7999065/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7999065/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e This study aimed to compare the diagnostic performance of two deep learning-based object detection algorithms—YOLOv5 and YOLOv8—for the automatic detection and anatomical classification of mandibular fractures on panoramic radiographs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A total of 400 panoramic radiographs with confirmed mandibular fractures were collected from the clinical archives of Dicle University, Faculty of Dentistry. The dataset was expanded to 980 images using data augmentation techniques (rotation, contrast adjustment, and flipping). Images were annotated according to five anatomical regions (symphysis, body, angle, ramus, and condyle). YOLOv5 and YOLOv8 models were trained on 80% of the dataset, validated on 10%, and tested on 10%. Model performance was evaluated using precision, recall, F1-score, mean average precision (mAP), and intersection over union (IoU).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e YOLOv8 achieved higher diagnostic accuracy compared to YOLOv5. The YOLOv8 model yielded 0.85 precision, 0.83 recall, 0.84 F1-score, 0.89 mAP, and 0.82 IoU, while YOLOv5 achieved 0.81, 0.78, 0.79, 0.84, and 0.77, respectively. Visual inspection of detection maps confirmed that YOLOv8 produced more stable bounding box predictions and better localization across all anatomical zones, particularly in the angle and condylar regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Both models demonstrated high potential for assisting clinicians in detecting mandibular fractures on panoramic radiographs. YOLOv8 exhibited superior precision and generalization ability, suggesting that advanced deep learning architectures may improve diagnostic workflows in dental trauma assessment.\u003c/p\u003e","manuscriptTitle":"Comparative Analysis of Yolov5 and Yolov8 Deep Learning Models in the Detection and Anatomical Classification of Mandibular Fractures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-07 13:31:08","doi":"10.21203/rs.3.rs-7999065/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":"84b8f137-00ea-4fc1-be4d-1f7a27ca2edd","owner":[],"postedDate":"November 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-04T06:23:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-07 13:31:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7999065","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7999065","identity":"rs-7999065","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}