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 Article Faculty Publications -Articles 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 Sahu, Premananda; Kumar, Ashwani; Singh, Mahesh; Jain, Rituraj; Upreti, Kamal; Parashar, Jyoti Title A name given to the resource LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection 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 Sakarya University Journal of Computer and Information Sciences;Volume;8;Issue;2;pp.184-197 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.35377/saucis...1665478" target="_blank" rel="noreferrer noopener">https://doi.org/10.35377/saucis...1665478</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010079253?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010079253?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 Sahu P., Lovely Professional University, School of CSE, Punjab, Jalandhar, India; Kumar A., Bennett University, School of Computer Science Engineering &amp; Technology, UP, Greater Noida, India; Singh M., Aditya University, Department of ECE, Surampalem, India; Jain R., Marwadi University, Department of Information Technology, Gujarat, Rajkot, India; Upreti K., Christ University, Department of Computer Science, Delhi NCR Campus, Ghaziabad, India; Parashar J., Bharati Vidyapeeth's Institute of Computer Applications &amp; Management, Department of Computer Application, Delhi, India Description An account of the resource Early and accurate diagnosis, however, is still lacking for the most common form of lung cancer, and this remains one of the leading cancers leading to mortality. CT scans are widely used for lung cancer screening; however, their manual interpretation is time-consuming and prone to variability. This study introduces LungDxNet, a deep learning-based framework that integrates transfer learning to enhance diagnostic accuracy and efficiency. Using a large dataset of Low Dose CT (LDCT) scans, the system is built with fine-tuned pre-trained Convolutional Neural Networks (CNNs) such that feature extraction is reliable though minimal reducing radiation exposure. Consequently, LungDxNet involves the integration of component segmentation techniques that have been used to isolate the lung regions and discriminate the cancerous nodules from the malignant and benign cases. Very rigorous evaluations were performed on the model against both conventional machine learning and state of the art deep learning architectures. Results show that there is a substantial reduction of false positive and false negative resulting in a superior accuracy (98.88), sensitivity, and specificity. This design is to be scaled, robust and clinically applicable, making it a potential real world lung cancer diagnosis tool. Deep learning and transfer learning has excellent power to transform lung cancer detection, and this research brings awareness of how far we can optimise and integrate into clinical workflow. The model is enhanced for future work and adapted for real time diagnostic applications. 2025, Sakarya University. All rights reserved. Subject The topic of the resource Artificial Intelligence; Deep Learning; Low Dose CT; Lung Cancer; Machine Learning Publisher An entity responsible for making the resource available Sakarya University Relation A related resource ISSN: 26368129; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online