Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource George, Jossy P.; Upreti, Kamal; Poonia, Ramesh Chandra; Alapatt, Bosco Paul Title A name given to the resource Deploying Deep Learning in Real-Time for Lung Cancer Diagnosis via Medical Imaging Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1144;pp.401-412 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-7839-3_28" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-7839-3_28</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85218462995?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85218462995?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant George J.P., Department of Computer Science, CHRIST (Deemed to Be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; Upreti K., Department of Computer Science, CHRIST (Deemed to Be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to Be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; Alapatt B.P., Department of Computer Science, CHRIST (Deemed to Be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India Description An account of the resource In this research, deep learning models were used to diagnose lung cancer automatically using hospital image data. A dataset with 3,400 lung cancer images from online repositories and hospital archives was used for model training and evaluation. After preprocessing and feature extraction, various deep learning architectures such as VGG-16, CNN, ResNet and RNN were adopted in this study. The VGG-16 model had the highest accuracy rate of 96.86%, showing strong performance. This rate of accuracy is actually higher than their accuracy of 91%. These results serve to highlight the impressive accuracy achieved by our study relative to prior research. By accurately and effectively altering lung cancer diagnosis into a process entirely reliant on algorithms, deep learning models show promise for their potential. Diagnostic tools should be able to catch cancer early and accurately, identify the present type and classification for tumors. For all its promise, limitations such as dataset size and generalizability mean that clinical trials will be needed for further validation. Focus should turn toward this as the direction of future research in order to enhance model robustness and applicability against challenges. This research allows us to better the well-being of patients and reduce the burden of lung cancer through timely intervention and personalized treatment strategies by making use of advanced techniques in medical diagnostics. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Automated; Deep learning; Diagnosis; Lung cancer; Medical imaging Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981977838-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online