Meta-Domain Adaptive Framework for Efficient Diagnostic Assessment of Lung Infection Using CT Radiographs

Meta-Domain Adaptive Framework for Efficient Diagnostic Assessment of Lung Infection Using CT Radiographs | 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 Meta-Domain Adaptive Framework for Efficient Diagnostic Assessment of Lung Infection Using CT Radiographs Muhammad Owais, Taimur Hassan, Naqash Afzal, Saddam Hussain Khan, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5252777/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Computed tomography (CT) scans are widely used for diagnosing lung infections, but manual interpretation is laborious. Artificial intelligence has spurred the development of efficient computer-aided diagnostic (CAD) systems, promising faster and more accurate diagnosis. However, many existing CAD systems lack sufficient cross-data analysis and consequently show suboptimal performance. To address their limitations, we propose a lightweight Meta-Domain Adoptive Segmentation Network (MDA-SN) with adaptive data normalization to enhance infection detection in cross-data analysis. Our optimal network design leverages multi-scale dilated grouped convolution with residual attention to ensure real-time performance and maintain accuracy. We further utilize the model to build a semantic attention-driven retrieval framework, enabling infection ratio quantification and retrieval of relevant CT slices from the database, closely matching the input test sample. Our method achieved an average cross-dataset performance of 75.93% Dice index and 67.42% Intersection over Union, surpassing state-of-the-art methods by 3.32% and 3.28%, respectively. Additionally, it achieves real-time execution, processing an average of 29 slices per second due to its significantly reduced number of training parameters—approximately 70% fewer than its closest competitor. Biological sciences/Cancer Health sciences/Biomarkers Health sciences/Medical research Lung lesion segmentation Adaptive data normalization Attention-driven retrieval MDA-SN Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Jun, 2025 Reviews received at journal 09 Jun, 2025 Reviewers agreed at journal 03 Jun, 2025 Reviewers agreed at journal 21 Nov, 2024 Reviews received at journal 06 Nov, 2024 Reviewers agreed at journal 30 Oct, 2024 Reviewers invited by journal 30 Oct, 2024 Editor assigned by journal 30 Oct, 2024 Editor invited by journal 15 Oct, 2024 Submission checks completed at journal 15 Oct, 2024 First submitted to journal 12 Oct, 2024 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-5252777","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":372368191,"identity":"f2aa975f-9f5b-40bc-a3e8-52aa7f593276","order_by":0,"name":"Muhammad 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Artificial intelligence has spurred the development of efficient computer-aided diagnostic (CAD) systems, promising faster and more accurate diagnosis. However, many existing CAD systems lack sufficient cross-data analysis and consequently show suboptimal performance. To address their limitations, we propose a lightweight Meta-Domain Adoptive Segmentation Network (MDA-SN) with adaptive data normalization to enhance infection detection in cross-data analysis. Our optimal network design leverages multi-scale dilated grouped convolution with residual attention to ensure real-time performance and maintain accuracy. We further utilize the model to build a semantic attention-driven retrieval framework, enabling infection ratio quantification and retrieval of relevant CT slices from the database, closely matching the input test sample. 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