<div>According to WHO, COVID-19 is an infectious disease and has a
significant social and economic impact. The main challenge in ?fighting
against this disease is its scale. Due to the imminent outbreak, the
medical facilities are over exhausted and unable to accommodate the
piling cases. A quick diagnosis system is required to address these
challenges. To this end, a stochastic deep learning model is proposed.
The main idea is to constrain the deep representations over a gaussian
prior to reinforce the discriminability in feature space. The model can
work on chest X-ray or CT-scan images. It provides</div>


<div>a fast diagnosis of COVID-19 and can scale seamlessly. This work
presents a comprehensive evaluation of previously proposed approaches
for X-ray based</div>


<div>disease diagnosis. Our approach works by learning a latent space over
X-ray image distribution from the ensemble of state-of-the-art
convolutional-nets,</div>


<div>and then linearly regressing the predictions from an ensemble of
classifi?ers which take the latent vector as input. We experimented with
publicly available datasets having three classes { COVID-19, normal,
Pneumonia. Moreover, for robust evaluation, experiments were performed
on a large chest X-ray dataset with fi?ve different very similar
diseases. Extensive empirical evaluation shows</div>


<div>how the proposed approach advances the state-of-the-art.</div>