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The ongoing COVID-19 pandemic has already taken millions of lives and damagedeconomies across the globe. Most COVID-19 deaths and economic losses arereported from densely crowded cities. It is comprehensible that the effectivecontrol and prevention of epidemic/pandemic infectious diseases is vital.According to WHO, testing and diagnosis is the best strategy to controlpandemics. Scientists worldwide are attempting to develop various innovativeand cost-efficient methods to speed up the testing process. This papercomprehensively evaluates the applicability of the recent top tenstate-of-the-art Deep Convolutional Neural Networks (CNNs) for automaticallydetecting COVID-19 infection using chest X-ray images. Moreover, it provides acomparative analysis of these models in terms of accuracy. This studyidentifies the effective methodologies to control and prevent infectiousrespiratory diseases. Our trained models have demonstrated outstanding resultsin classifying the COVID-19 infected chest x-rays. In particular, our trainedmodels MobileNet, EfficentNet, and InceptionV3 achieved a classificationaverage accuracy of 95\%, 95\%, and 94\% test set for COVID-19 classclassification, respectively. Thus, it can be beneficial for clinicalpractitioners and radiologists to speed up the testing, detection, andfollow-up of COVID-19 cases.

Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Images