An Optimized EfficientNet-B3 Based Lung Cancer Detection Framework with Stratified Splitting and Deployment-Ready Torch Script Integration

An Optimized EfficientNet-B3 Based Lung Cancer Detection Framework with Stratified Splitting and Deployment-Ready Torch Script Integration | 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 An Optimized EfficientNet-B3 Based Lung Cancer Detection Framework with Stratified Splitting and Deployment-Ready Torch Script Integration Sugandha Saxena, S N Prasad, Preethi Preethi, Chaya Ravindra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9199652/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 Lung cancer still remains one of the major causes of cancer deaths worldwide, which in turn indicates the need for efficient, accurate, and automated diagnostic support systems. In this context, this paper proposes a completely automated system for the diagnosis of lung cancer using chest computed tomography scans, specifically using a deep learning approach and the EfficientNet-B3 model as the backbone for the proposed system. To enhance the generalization of the proposed system, despite the limited dataset of 1,097 images, stratified data splitting, optimized data augmentation, and adaptive learning rate schedules are employed in the proposed system. To ensure class balance in the dataset, a stratified data splitting approach of 70-20-10 was used for the proposed system, where the dataset was divided into 70% for training, 20% for validation, and 10% for testing. During the training process, the system converged well, achieving a high accuracy of 99.48%, a minimal loss of 0.0197, and a maximum validation accuracy of 99.09%. Additionally, the generalization performance of the proposed system, in terms of accuracy and loss, on the test dataset was found to be high, achieving a maximum accuracy of 98% and a minimum loss of 0.0566, thereby validating the effectiveness of the proposed system in accurately classifying lung cancer, benign, and normal images. Thus, the proposed system, specifically using the EfficientNet-B3 model, optimized preprocessing, and a well-designed system, presents a promising and efficient solution for the automated diagnosis of lung cancer. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing Lung Cancer Detection EfficientNet-B3 Deep Learning CT Imaging Stratified Splitting TorchScript Deployment Medical Image Classification Computer-Aided Diagnosis CNN-Based Classification Automated Diagnosis 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. 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-9199652","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":617703710,"identity":"99fc18f3-32d5-4e54-a3f2-f56de4abe137","order_by":0,"name":"Sugandha Saxena","email":"","orcid":"","institution":"Dayananda Sagar University","correspondingAuthor":false,"prefix":"","firstName":"Sugandha","middleName":"","lastName":"Saxena","suffix":""},{"id":617703713,"identity":"350da916-1c4b-4e31-92bf-fe7323315737","order_by":1,"name":"S N Prasad","email":"","orcid":"","institution":"Dayananda Sagar University","correspondingAuthor":false,"prefix":"","firstName":"S","middleName":"N","lastName":"Prasad","suffix":""},{"id":617703720,"identity":"3da93269-a84f-432d-b9ba-fd3bc3db65f7","order_by":2,"name":"Preethi Preethi","email":"data:image/png;base64,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","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":true,"prefix":"","firstName":"Preethi","middleName":"","lastName":"Preethi","suffix":""},{"id":617703725,"identity":"4f6b9e89-50a5-41c9-9942-3485e41deab0","order_by":3,"name":"Chaya Ravindra","email":"","orcid":"","institution":"T.John Institute of Technology,","correspondingAuthor":false,"prefix":"","firstName":"Chaya","middleName":"","lastName":"Ravindra","suffix":""}],"badges":[],"createdAt":"2026-03-23 11:09:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9199652/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9199652/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106846654,"identity":"d2f4730a-c535-4bb5-bda6-0931042aa1fd","added_by":"auto","created_at":"2026-04-14 05:10:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":928354,"visible":true,"origin":"","legend":"","description":"","filename":"stratifiedlungSM2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9199652/v1_covered_164befc5-42e7-444b-ad5f-d21f59b170fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Optimized EfficientNet-B3 Based Lung Cancer Detection Framework with Stratified Splitting and Deployment-Ready Torch Script Integration","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"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 Detection, EfficientNet-B3, Deep Learning, CT Imaging, Stratified Splitting, TorchScript Deployment, Medical Image Classification, Computer-Aided Diagnosis, CNN-Based Classification, Automated Diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-9199652/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9199652/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLung cancer still remains one of the major causes of cancer deaths worldwide, which in turn indicates the need for efficient, accurate, and automated diagnostic support systems. 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