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 Samal, Sonali; Champatiray, Chiranjibi Title A name given to the resource Image Data Driven Lung Cancer Classification using Deep Learning and Optimization Algorithm 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 1st IEEE International Conference on Data Science and Intelligent Network Computing, ICDSINC 2025;pp.53-57 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDSINC66221.2025.11448185" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDSINC66221.2025.11448185</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037444824?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037444824?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 Samal S., Department of Computer Science and Engineering, Alliance School of Advanced Computing, Alliance University, Bengaluru, India; Champatiray C., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, Christ University, Bengaluru, India Description An account of the resource Lung cancer continues to be a major contributor to global cancer mortality, underscoring the importance of early detection and accurate diagnosis. This work introduces an integrated framework that leverages deep learning in combination with Bayesian optimization to achieve robust lung cancer classification. Convolutional Neural Networks (CNNs) are employed for feature extraction and image analysis, while Bayesian optimization is applied to automatically fine-tune critical hyperparameters, thereby improving accuracy and minimizing training overhead. The methodology focuses on the analysis of computed tomography (CT) images to distinguish between different lung cancer categories. By addressing the limitations of manual hyperparameter selection, the proposed framework enhances the efficiency and reliability of deep learning models in medical imaging. The outcomes of this study highlight its potential contribution to computer-aided diagnosis, offering clinicians an effective decision-support tool for precise and timely lung cancer detection. 2025 IEEE. Subject The topic of the resource Batch Size Selection; CNN; Hyperparameter Tuning; Learning Rate Optimization Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156524-4; 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