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 Babu Kumar, S.; Vinodha, D.; Geetha, P.; Jenefa, J.; Anoop, G.L.; Santhrupth, B.C. Title A name given to the resource Ensemble Deep Learning for COVID-19 Detection Using Multi-Modal 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 Proceedings of 2025 IEEE International Conference on Contemporary Computing and Communications, InC4 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/InC465408.2025.11256190" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/InC465408.2025.11256190</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030335326?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030335326?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 Babu Kumar S., CHRIST University, Department of Computer Science and Engineering, Bengaluru, India; Vinodha D., CHRIST University, Department of Computer Science and Engineering, Bengaluru, India; Geetha P., SRM Institute of Science and Technology, Department of Computational Intelligence, Kattankulathur, India; Jenefa J., CHRIST University, Department of Computer Science and Engineering, Bengaluru, India; Anoop G.L., CHRIST University, Department of Computer Science and Engineering, Bengaluru, India; Santhrupth B.C., CHRIST University, Department of Computer Science and Engineering, Bengaluru, India Description An account of the resource The COVID-19 pandemic has had a profound impact worldwide This work proposes a deep ensemble learning model incorporating multi-modal inputs, i.e., CT scans and Xrays, to classify the cases into COVID-19, Viral Pneumonia, or Normal. Employing an ensemble average voting approach from three different CNN models InceptionV3, DenseNet-169, and Xception the suggested methodology is highly accurate and reliable. Preprocessing methods such as Contrast Limited Adaptive Histogram Equalization (CLAHE) improve data quality, and Local Interpretable Model-Agnostic Explanations (LIME) allow interpretable prediction through identification of major image features driving classifications. The ensemble model suggested attains an accuracy of 99.64%, outperforming single models, with precision at 99.50%, recall at 99.73%, and an F1-score of 99.61%, which makes it very reliable for detecting COVID-19. Comparative analysis shows that our ensemble method performs better than individual CNN architectures, such as Xception (99.18%), ResNet101 (98.95%), and DenseNet201 (98.83%), which showcases its better diagnostic performance. 2025 IEEE. Subject The topic of the resource Classification; CLEHE; Covid-19; Ensemble Average Voting; Ensemble Model; InceptionV3; LIME Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152118-9; 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