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 Articles 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/. Title A name given to the resource SVD-CLAHE boosting and balanced loss function for Covid-19 detection from an imbalanced Chest X-Ray dataset Subject The topic of the resource Categorical Cross Entropy (CCE); Chest X-Ray (CXR) images; Class imbalance problem; Contrast Limited Adaptive Histogram Equalization (CLAHE); Covid-19 detection; Data augmentation; Singular Value Decomposition (SVD) Description An account of the resource Covid-19 disease has had a disastrous effect on the health of the global population, for the last two years. Automatic early detection of Covid-19 disease from Chest X-Ray (CXR) images is a very crucial step for human survival against Covid-19. In this paper, we propose a novel data-augmentation technique, called SVD-CLAHE Boosting and a novel loss function Balanced Weighted Categorical Cross Entropy (BWCCE), in order to detect Covid 19 disease efficiently from a highly class-imbalanced Chest X-Ray image dataset. Our proposed SVD-CLAHE Boosting method is comprised of both oversampling and under-sampling methods. First, a novel Singular Value Decomposition (SVD) based contrast enhancement and Contrast Limited Adaptive Histogram Equalization (CLAHE) methods are employed for oversampling the data in minor classes. Simultaneously, a Random Under Sampling (RUS) method is incorporated in major classes, so that the number of images per class will be more balanced. Thereafter, Balanced Weighted Categorical Cross Entropy (BWCCE) loss function is proposed in order to further reduce small class imbalance after SVD-CLAHE Boosting. Experimental results reveal that ResNet-50 model on the augmented dataset (by SVD-CLAHE Boosting), along with BWCCE loss function, achieved 95% F1 score, 94% accuracy, 95% recall, 96% precision and 96% AUC, which is far better than the results by other conventional Convolutional Neural Network (CNN) models like InceptionV3, DenseNet-121, Xception etc. as well as other existing models like Covid-Lite and Covid-Net. Hence, our proposed framework outperforms other existing methods for Covid-19 detection. Furthermore, the same experiment is conducted on VGG-19 model in order to check the validity of our proposed framework. Both ResNet-50 and VGG-19 model are pre-trained on the ImageNet dataset. We publicly shared our proposed augmented dataset on Kaggle website (https://www.kaggle.com/tr1gg3rtrash/balanced-augmented-covid-cxr-dataset), so that any research community can widely utilize this dataset. Our code is available on GitHub website online (https://github.com/MrinalTyagi/SVD-CLAHE-and-BWCCE). 2022 Elsevier Ltd Creator An entity primarily responsible for making the resource Roy S.; Tyagi M.; Bansal V.; Jain V. Source A related resource from which the described resource is derived Computers in Biology and Medicine, Vol-150 Publisher An entity responsible for making the resource available Elsevier Ltd 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.compbiomed.2022.106092" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.compbiomed.2022.106092</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139186391&amp;doi=10.1016%2Fj.compbiomed.2022.106092&amp;partnerID=40&amp;md5=ecb10649aab6bb3bd33b0106190d182c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139186391&amp;doi=10.1016%2fj.compbiomed.2022.106092&amp;partnerID=40&amp;md5=ecb10649aab6bb3bd33b0106190d182c</a> Rights Information about rights held in and over the resource All Open Access; Green Open Access Relation A related resource ISSN: 104825; PubMed ID: 36208598; CODEN: CBMDA 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 Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Roy S., School of Engineering and Technology, Christ (Deemed to be University), Bangalore, 560074, India; Tyagi M., Bharati Vidyapeeth's College of Engineering, New Delhi, 110063, India; Bansal V., Bharati Vidyapeeth's College of Engineering, New Delhi, 110063, India; Jain V., School of Computer Science Engineering and Technology, Bennett University, UP, Greater Noida, 201310, India