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 Cheekolu, Karunakar; Babu Kumar, S.; Raju, G. Title A name given to the resource Lung Cancer Classification from CT-Scan Images Using an Enhanced VGG16 Model Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1614 LNNS;pp.467-480 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-95-2878-3_33" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-95-2878-3_33</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029561054?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029561054?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 Cheekolu K., Department of Computer Science and Engineering, CHRIST University, Bengaluru, India; Babu Kumar S., Department of Computer Science and Engineering, CHRIST University, Bengaluru, India; Raju G., Department of Computer Science and Engineering, CHRIST University, Bengaluru, India Description An account of the resource Lung cancer has been one of the most common and deadly types of cancer around the globe, for which early detection is quite crucial for patient survival. In this research work, a deep learning-based method for four-class classification of chest CT-scan images, such as Squamous Cell Carcinoma, Large Cell Carcinoma, Adenocarcinoma, and Normal, is presented. With a modified VGG16 architecture, adding Squeeze-and-Excitation (SE) blocks and residual connections, the enhanced SERES_VGG16 model enhances feature representation and classification accuracy. The dataset we used here contains preprocessed chest CT-scan images divided into a training set, validation set, and test set. It is trained with augmentation techniques in the data to improve generalization. Its performance is evaluated using measures of standard performances, such as F1-score, recall, precision, accuracy and confusion matrices. The model achieved over 95% accuracy, class-wise precision ranging from 94 to 99%, recall ranging from 88 to 99%, F1-score from 93 to 96%. The presented approach reached over 95% accuracy on the test set and can be a trusted second opinion for radiologists to assist with early and accurate lung cancer subtype classification. However, this study is constrained by the small size of the dataset and the lack of other clinical parameters like genetic information. Future studies will concentrate on expanding the dataset and integrating multi-modal clinical information for enhanced robustness. This work in this study justifies the importance of deep learning in the classification of the medical images and points out further ways toward improving automated diagnostic systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource CT scans; Lung cancer; Residual blocks; Squeeze-and-Excitation; VGG Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981952877-6; 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