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 Augmentation and Preprocessing Techniques for Improved Generalization Performance in Deep Learning based Chest X-Ray Classification Subject The topic of the resource Augmentation; CNN; ResNet; Transfer Learning; X Ray Description An account of the resource Convolutional Neural Network (CNN) models are well known for image classification; however, the downside of CNN is the ineffectiveness to generalize and inclination towards over-fitting in case of a small train dataset. A balanced and sufficient data is thus essential to effectively train a CNN model, but this is not always possible, especially in the case of medical imaging data, as often patients with the same disease are not always available. Image augmentation addresses the given issue by creating new data points artificially with slight modifications. This study, investigates ten different methods with various parameters and probability and their combined effect on the test dataset's generalization performance and F1 Score. For the study, three pre-Trained CNN models, namely ResNetl8, ResNet34, and ResNet50, are fine tuned on a small training dataset of 500 Pneumonia and 160 Non-Pneumonia(Normal) Images for each augmentation setting. The test accuracy, F1 Score, and generalization performance were calculated for a test dataset consisting of 50 Pneumonia and 16 Non-Pneumonia(Normal) Images. 2022 IEEE. Creator An entity primarily responsible for making the resource Prakash S.; Ramamurthy B. Source A related resource from which the described resource is derived 2022 International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2022 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. 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/10.1109/TQCEBT54229.2022.10041560" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT54229.2022.10041560</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149184914&amp;doi=10.1109%2FTQCEBT54229.2022.10041560&amp;partnerID=40&amp;md5=bb6f44f7217424455a2969c4e6eb131c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149184914&amp;doi=10.1109%2fTQCEBT54229.2022.10041560&amp;partnerID=40&amp;md5=bb6f44f7217424455a2969c4e6eb131c</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166545361-5 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 Prakash S., CHRIST (Deemed to Be University), Computer Science Department, Bengaluru, India; Ramamurthy B., CHRIST (Deemed to Be University), Computer Science Department, Bengaluru, India