  The world is going through a challenging phase due to the disastrous effect
caused by the COVID-19 pandemic on the healthcare system and the economy. The
rate of spreading, post-COVID-19 symptoms, and the occurrence of new strands of
COVID-19 have put the healthcare systems in disruption across the globe. Due to
this, the task of accurately screening COVID-19 cases has become of utmost
priority. Since the virus infects the respiratory system, Chest X-Ray is an
imaging modality that is adopted extensively for the initial screening. We have
performed a comprehensive study that uses CXR images to identify COVID-19 cases
and realized the necessity of having a more generalizable model. We utilize
MobileNetV2 architecture as the feature extractor and integrate it into Capsule
Networks to construct a fully automated and lightweight model termed as
MobileCaps. MobileCaps is trained and evaluated on the publicly available
dataset with the model ensembling and Bayesian optimization strategies to
efficiently classify CXR images of patients with COVID-19 from non-COVID-19
pneumonia and healthy cases. The proposed model is further evaluated on two
additional RT-PCR confirmed datasets to demonstrate the generalizability. We
also introduce MobileCaps-S and leverage it for performing severity assessment
of CXR images of COVID-19 based on the Radiographic Assessment of Lung Edema
(RALE) scoring technique. Our classification model achieved an overall recall
of 91.60, 94.60, 92.20, and a precision of 98.50, 88.21, 92.62 for COVID-19,
non-COVID-19 pneumonia, and healthy cases, respectively. Further, the severity
assessment model attained an R$^2$ coefficient of 70.51. Owing to the fact that
the proposed models have fewer trainable parameters than the state-of-the-art
models reported in the literature, we believe our models will go a long way in
aiding healthcare systems in the battle against the pandemic.
