Federated Learning for Lung Cancer Detection: Comparative Analysis and Visual Interpretability

Federated Learning for Lung Cancer Detection: Comparative Analysis and Visual Interpretability | 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 Federated Learning for Lung Cancer Detection: Comparative Analysis and Visual Interpretability Osei isaac, Iven Aabaah, Benjamin Appiah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6032484/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 Artificial Intelligence (AI) has become a crucial tool in the detection of lung can-cer through medical image segmentation. However, traditional AI approaches, which require centralizing sensitive patient data for model training, raise sig-nificant privacy concerns. This project investigates the efficiency of Federated Learning (FL) frameworks in comparison to a conventional centralized AI model. We evaluated seven different state-of-the art Federated Learning frameworks to assess their performance in maintaining model accuracy and scalability. Among these, Per-FedAvg and FedOpt demonstrated efficiency compared to the cen-tralized framework. To further understand the performance of these models, we utilized Gradient-weighted Class Activation Mapping (Grad-CAM) to visually interpret their predictions, ensuring that they focus on medically relevant fea-tures. This project highlights that Federated Learning frameworks, specifically Per-FedAvg and FedOpt, offer promising alternatives to traditional AI methods by providing enhanced performance in lung cancer detection. Bioinformatics Swin Transformer Detection Transformer 3D UNet 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-6032484","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":417033550,"identity":"489fdf0e-f7fd-4a52-85a9-ad5688dd4f4d","order_by":0,"name":"Osei isaac","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYJCCAwkgkr2BgZlELTwHSNACARIJRGrh5z988MCDP3by/DPfGH4uqLBh4G/vTsCrRXJGWsKBxLZkwxm3c4ylZ5xJY5A4c3YDXi0GN3gMDiQ2MDNukM4xkOZtO8xgIJFLQMv58x8OJPypt98gecb4N3FaDuQAQ4ztcOIGCR4z4mwB+gXosLbjyUBvlFnznEnjIegXYIg9/vjjT7Vtf/vhzbd5Kmzk+Nt78WtBAhwGIJKHWOUgwP6AFNWjYBSMglEwggAAAiVIu1K1y0IAAAAASUVORK5CYII=","orcid":"","institution":"Ho Technical University","correspondingAuthor":true,"prefix":"","firstName":"Osei","middleName":"","lastName":"isaac","suffix":""},{"id":417034067,"identity":"0b46150b-640c-43fa-883c-c8868cf85536","order_by":1,"name":"Iven Aabaah","email":"","orcid":"","institution":"C.K. 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