Edge-Aware Contrast Enhancement Framework to Aid Automated Bone Fracture Detection

Edge-Aware Contrast Enhancement Framework to Aid Automated Bone Fracture 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 Edge-Aware Contrast Enhancement Framework to Aid Automated Bone Fracture Detection Farhan Masud Nayem, Fahmid Al Farid, Abdullah Al Shiam, Hridoy Sutradhar, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8507893/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 Bone fracture detection from X-ray images is a challenging task due to low contrast, noise, and poor visibility of fine structural details. Existing enhancement techniques often fail to preserve clinically important edges, resulting in reduced diagnostic accuracy in automated systems. To address this limitation, this study proposes an Edge-Aware Contrast Enhancement Framework (EACEF) that integrates laplacian sharpening, sobel gradient extraction, smoothing, unsharp masking, and adaptive gamma correction to highlight fracture lines while suppressing noise. Three deep learning models, CNN, VGG16, and ResNet50, were trained on two datasets that included both fractured and non-fractured bone images. Experimental results show that the proposed preprocessing technique significantly improves classification performance. For Dataset-I, ResNet50 achieved a highest accuracy of 96%, outperforming CNN and VGG16. For Dataset-II, ResNet50 achieved a further 99% accuracy. The findings indicate that the proposed EACEF method effectively improves bone structure and fracture boundaries, improving the reliability of the automatic fracture detection system. This work highlights the importance of edge-aware preprocessing in medical imaging and lays a strong foundation for future advances in computer-aided diagnosis. CNN VGG16 ResNet50 Bone Fracture Detection Laplacian Sharpening Edge-Aware Contrast Enhancement 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. 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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-8507893","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577890173,"identity":"3a72c48d-1766-4c0b-9954-d60f5317c844","order_by":0,"name":"Farhan Masud Nayem","email":"","orcid":"","institution":"Netrokona University","correspondingAuthor":false,"prefix":"","firstName":"Farhan","middleName":"Masud","lastName":"Nayem","suffix":""},{"id":577890174,"identity":"311a9adf-5524-4f7d-a79a-7054a0b081a2","order_by":1,"name":"Fahmid Al Farid","email":"","orcid":"","institution":"Berlin School of Business and 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Existing enhancement techniques often fail to preserve clinically important edges, resulting in reduced diagnostic accuracy in automated systems. To address this limitation, this study proposes an Edge-Aware Contrast Enhancement Framework (EACEF) that integrates laplacian sharpening, sobel gradient extraction, smoothing, unsharp masking, and adaptive gamma correction to highlight fracture lines while suppressing noise. Three deep learning models, CNN, VGG16, and ResNet50, were trained on two datasets that included both fractured and non-fractured bone images. Experimental results show that the proposed preprocessing technique significantly improves classification performance. For Dataset-I, ResNet50 achieved a highest accuracy of 96%, outperforming CNN and VGG16. For Dataset-II, ResNet50 achieved a further 99% accuracy. 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