  The novel coronavirus (COVID-19), a highly infectious respiratory disease
caused by the SARS-CoV-2 has emerged as an unprecedented healthcare crisis. The
pandemic had a devastating impact on the health, well-being, and economy of the
global population. Early screening and diagnosis of symptomatic patients plays
crucial role in isolation of patient to help stop community transmission as
well as providing early treatment helping in reducing the mortality rate.
Although, the RT-PCR test is the gold standard for COVID-19 testing, it is a
manual, laborious, time consuming, uncomfortable, and invasive process. Due to
its accessibility, availability, lower-cost, ease of sanitisation, and portable
setup, chest X-Ray imaging can serve as an effective screening and diagnostic
tool. In this study, we first highlight limitations of existing datasets and
studies in terms of data quality, data imbalance, and evaluation strategy.
Second, we curated a large-scale COVID-19 chest X-ray dataset from many
publicly available COVID-19 imaging databases and proposed a pre-processing
pipeline to improve quality of the dataset. We proposed CoVScreen, an CNN
architecture to train and test the curated dataset. The experimental results
applying different classification scenarios on the curated dataset in terms of
various evaluation metrics demonstrate the effectiveness of proposed
methodology in the screening of COVID-19 infection.
