Shedding Light on Diagnostic Precision: GANs for Low Light Endoscopy Image Enhancements

Shedding Light on Diagnostic Precision: GANs for Low Light Endoscopy Image Enhancements | 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 Article Shedding Light on Diagnostic Precision: GANs for Low Light Endoscopy Image Enhancements Muhammad Asif, Hong Song, Muhammad Azam Zia, Sajid Ali, Emad Mahrous Awwad, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4213321/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 Understanding and identifying polyps in their early stages is crucial, and computerized diagnostic systems can significantly boost the effectiveness of screening through a large volume of endoscopy images. To achieve this, it's essential to enhance the quality of these images. In our research, we employ a well-established model called CycleGAN, introduced by Zhu et al. , which utilizes Generative Adversarial Networks (GANs). The CycleGAN model is trained on a diverse natural image database called RAISE and then tested on low illuminance endoscopy images from the Kvasir dataset, specifically created for polyp segmentation. Since there's no established ground truth for low illuminance endoscopy images, we use the non-reference Naturalness Image Quality Evaluator (NIQE) parameter to evaluate the quality of the generated high illuminance images in comparison to their low illuminance counterparts. Our main focus is to showcase how this image enhancement technique can be a valuable preprocessing step to avoid missing polyp detection during subsequent image analysis. Both visual observations and quantitative results suggest that our proposed method has the potential to effectively enhance endoscopic images analysis, contributing to an overall improvement in accuracy when it comes to polyp detection. Biological sciences/Biological techniques/Imaging/Endoscopy Health sciences/Medical research Endoscopy image analysis Image enhancement techniques Polyp Detection CycleGAN model Computerized diagnostic systems 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. 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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-4213321","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":292415339,"identity":"891c3e56-e789-4ec0-97b7-0b7cef1e5ef6","order_by":0,"name":"Muhammad Asif","email":"","orcid":"","institution":"Information Technology University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Asif","suffix":""},{"id":292415340,"identity":"de69043c-fd46-49e6-96d8-9859a14f7d14","order_by":1,"name":"Hong Song","email":"","orcid":"","institution":"Beijing Institute of 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