  The gold standard for COVID-19 is RT-PCR, testing facilities for which are
limited and not always optimally distributed. Test results are delayed, which
impacts treatment. Expert radiologists, one of whom is a co-author, are able to
diagnose COVID-19 positivity from Chest X-Rays (CXR) and CT scans, that can
facilitate timely treatment. Such diagnosis is particularly valuable in
locations lacking radiologists with sufficient expertise and familiarity with
COVID-19 patients. This paper has two contributions. One, we analyse literature
on CXR based COVID-19 diagnosis. We show that popular choices of dataset
selection suffer from data homogeneity, leading to misleading results. We
compile and analyse a viable benchmark dataset from multiple existing
heterogeneous sources. Such a benchmark is important for realistically testing
models. Our second contribution relates to learning from imbalanced data.
Datasets for COVID X-Ray classification face severe class imbalance, since most
subjects are COVID -ve. Twin Support Vector Machines (Twin SVM) and Twin Neural
Networks (Twin NN) have, in recent years, emerged as effective ways of handling
skewed data. We introduce a state-of-the-art technique, termed as Twin
Augmentation, for modifying popular pre-trained deep learning models. Twin
Augmentation boosts the performance of a pre-trained deep neural network
without requiring re-training. Experiments show, that across a multitude of
classifiers, Twin Augmentation is very effective in boosting the performance of
given pre-trained model for classification in imbalanced settings.
