Automated Chest Cancer Detection and Classification Using Deep Learning

Automated Chest Cancer Detection and Classification Using Deep Learning | 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 Automated Chest Cancer Detection and Classification Using Deep Learning Vijayalakshmi C, Dileep Kumar. Y This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8802977/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Early detection of chest cancer is critical for effective treatment and improved patient outcomes. This study proposes a structured workflow for automated chest cancer detection using a novel hybrid deep learning model, CTAF-Net. The workflow begins with robust preprocessing of CT scan data, including resizing, normalization, and data augmentation, to ensure high-quality inputs for the model, spatial features such as edges, textures, and shapes are extracted from the pre-processed images. To improve model efficiency and performance, Ant Colony Optimization (ACO) is employed to select an optimal subset of features from the extracted set, reducing redundancy and computational complexity. Finally, the CTAF-Net model, integrating Convolutional Neural Networks (CNN) and Vision Transformer modules, classifies the selected features to accurately identify cancerous and non-cancerous cases. Finally, the performance evaluation stage assesses the model using metrics such as accuracy, precision, recall, and F1-score, providing quantitative evidence of its effectiveness. The proposed deep learning framework outperforms baseline models, achieving 99% accuracy, 98.9% precision, 99% recall, and 98.95% F1-Score, representing an improvement of 0.9–1% over the best baseline (InceptionV3). This demonstrates enhanced sensitivity to subtle tumor patterns, reduced false predictions, and superior overall classification performance. CT Scan Imaging Medical Image Analysis Vision Transformer Convolutional Neural Network Data Augmentation Performance Evaluation Computer-Aided Diagnosis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 10 Mar, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviewers invited by journal 01 Mar, 2026 Editor assigned by journal 15 Feb, 2026 Submission checks completed at journal 13 Feb, 2026 First submitted to journal 06 Feb, 2026 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-8802977","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600336406,"identity":"0a485a77-d9f5-40c2-b000-baae6a39f4f3","order_by":0,"name":"Vijayalakshmi C","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABKUlEQVRIie2PMUvDQBTHXwxclqu3vnCSz3CQpYIf5kLgXCLo4iQqFNLBqHMQv0OmzgEhXeIoJHTRpV26FUrBol7UqSTtKni/4XH8eb977wEYDH8RG6Sun94+EP0GvLp3rnUg+rsU2ye/ipUmeaPgzllB/KOAlVXNH9CtiDG8LegF8YlThIuzqG+76ewlW50isOGNbFWeIOS0oB6hquDpCAnj0Xmd6MWwfM7aFHcAklOC+pbj2O6NkLoPkaqoVgSedCnhO/0QQcxmg4VWUNSlqtdbFGaD4r1YBjGqnGtFiMopJtumNMrh413uE5yqRpFuEpHJgUDadQthZVjNl7nHmPL1YpeSOeNpPV8feWx426oAULlHNxLxXdvbG5zcWm0kr93dBoPB8B/5AgRrWzRgBsJmAAAAAElFTkSuQmCC","orcid":"","institution":"Mohan babu University","correspondingAuthor":true,"prefix":"","firstName":"Vijayalakshmi","middleName":"","lastName":"C","suffix":""},{"id":600336413,"identity":"89fd10f5-9172-4ce4-bfcf-665c16a169d2","order_by":1,"name":"Dileep Kumar. 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