High-Precision Lung Cancer Localization Precision with Histogram Equalisation and Frequency-Domain Hybrid Attention

High-Precision Lung Cancer Localization Precision with Histogram Equalisation and Frequency-Domain Hybrid Attention | 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 High-Precision Lung Cancer Localization Precision with Histogram Equalisation and Frequency-Domain Hybrid Attention Shiqiang BAI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9280366/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 advent of deep learning algorithms, in particular Convolutional Neural Networks, has provided robust technical support for medical image processing. We propose solutions to the challenges of poor image quality, insufficient model accuracy, and lost frequency domain information. These solutions include a histogram equalisation module, a gated feature selection mechanism, and a hybrid frequency-domain attention module. The integration of these three modules into the U-Net architecture has been demonstrated to enhance performance through the implementation of feature filtering and fine-grained frequency domain capture. Compared with the baseline U-Net model, the Histogram Enhancement–Fourier Transform & Laplace Transform Attention U-Net (HFLU-Net) improved the accuracy metric for localising small-scale lung cancer lesions to 0.6889. This finding indicates that the model contributes to the enhancement of radiotherapy precision for patients with early-stage lung cancer. Nuclear Medicine & Medical Imaging Artificial Intelligence and Machine Learning Lung Cancer Convolutional Neural Network Gated Feature Extraction Hybrid Frequency-domain Attention HLFU-Net Full Text Additional Declarations The authors declare no competing interests. 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-9280366","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615308989,"identity":"b4a382f3-2a85-49d5-a4c3-cde560cca1a8","order_by":0,"name":"Shiqiang BAI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie3PsQsBURzA8Z+uTHfd+nTlb3hSJ3XlX/mVepMLi25DdDcgK/+FCZvHlekx36CQMrOIRW4n92yG9x1+0+/Trx+ASvWH0X2fhnfv2VwMg+UBPUeCRCJ3AsFTEyLK9CCYDKnk86luTADtzNFfSRHbqrd2WgE48zDNwQx6+JWUIndqjefndLHTXkeo74CIzSThijslhtB04JxFSM5AiZtIZnfD1whwtGtIQxlSvVmGH1IaE0CUIttGZiQYxqNMkDM9+Zd93yYXz0HTDJbXx9PJmsHgO3lL/21dpVKpVB97ATFlWum2rD6lAAAAAElFTkSuQmCC","orcid":"","institution":"Intelligent medical imaging perception laboratory","correspondingAuthor":true,"prefix":"","firstName":"Shiqiang","middleName":"","lastName":"BAI","suffix":""}],"badges":[],"createdAt":"2026-03-31 13:12:29","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9280366/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9280366/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105905335,"identity":"9bdabdf3-bb1d-4e66-bc97-b6efb2cbcaa8","added_by":"auto","created_at":"2026-04-01 10:11:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1936303,"visible":true,"origin":"","legend":"","description":"","filename":"HighPrecisionLungCancerLocalizationPrecisionwithHistogramEqualisationandFrequencyDomainHybridAttention.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9280366/v1_covered_a91e3007-5546-4da2-8ecc-4dcc443dd986.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eHigh-Precision Lung Cancer Localization Precision with Histogram Equalisation and Frequency-Domain Hybrid Attention\u003c/strong\u003e\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":true,"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":"Lung Cancer, Convolutional Neural Network, Gated Feature Extraction, Hybrid Frequency-domain Attention, HLFU-Net","lastPublishedDoi":"10.21203/rs.3.rs-9280366/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9280366/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eThe advent of deep learning algorithms, in particular Convolutional Neural Networks, has provided robust technical support for medical image processing. 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