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 Conference Papers 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/. Title A name given to the resource Comparison of Affine and DCGAN-based Data Augmentation Techniques for Chest X-Ray Classification Subject The topic of the resource Affine; Data Augmentation; Deep Learning; Generative adversarial networks (GANs); Medical Domain Description An account of the resource Data augmentation, also called implicit regularization, is one of the popular strategies to improve the generalization capability of deep neural networks. It is crucial in situations where there is a scarcity of high-quality ground-truth data. Also getting new samples is expensive and time consuming. This is a typical issue in the medical domain. Therefore, this study compares the performance of Affine and Generative Adversarial Networks (GAN)- based data augmentation techniques on the chest image X-Ray dataset. The Pneumonia dataset contains 5863 chest X-Ray images. The traditional Affine data augmentation technique is applied as a pre-processing technique to various deep learning-based CNN models like VGG16, Inception V3, InceptionResNetV2, DenseNet-169 and DenseNet-202 to compare their performance. On the other hand, DCGAN architecture is applied to the dataset for augmentation. Evaluation measures like accuracy, recall and AUC depict that DCGAN outperforms other traditional models. The most important advantage of DCGAN is that it is able to identify fake images with 100% accuracy. This is especially relevant for the medical domain as it deals with the life of individuals. Thus, it can be concluded that DCGAN has better performance as compared to affine transformations applied to traditional CNN models. 2023 The Authors. Published by Elsevier B.V. Creator An entity primarily responsible for making the resource Bali M.; Mahara T. Source A related resource from which the described resource is derived Procedia Computer Science, Vol-218, pp. 283-290. Publisher An entity responsible for making the resource available Elsevier B.V. Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.procs.2023.01.010" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.procs.2023.01.010</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163729526&amp;doi=10.1016%2Fj.procs.2023.01.010&amp;partnerID=40&amp;md5=7e8b38ba8634fd2cec389ff5d8a16fb8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163729526&amp;doi=10.1016%2fj.procs.2023.01.010&amp;partnerID=40&amp;md5=7e8b38ba8634fd2cec389ff5d8a16fb8</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 18770509 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Bali M., SBM, Christ University, Bangalore, 560029, India; Mahara T., SBM, Christ University, Bangalore, 560029, India