  Coronaviruses constitute a family of viruses that gives rise to respiratory
diseases. As COVID-19 is highly contagious, early diagnosis of COVID-19 is
crucial for an effective treatment strategy. However, the RT-PCR test which is
considered to be a gold standard in the diagnosis of COVID-19 suffers from a
high false-negative rate. Chest X-ray (CXR) image analysis has emerged as a
feasible and effective diagnostic technique towards this objective. In this
work, we propose the COVID-19 classification problem as a three-class
classification problem to distinguish between COVID-19, normal, and pneumonia
classes. We propose a three-stage framework, named COV-ELM. Stage one deals
with preprocessing and transformation while stage two deals with feature
extraction. These extracted features are passed as an input to the ELM at the
third stage, resulting in the identification of COVID-19. The choice of ELM in
this work has been motivated by its faster convergence, better generalization
capability, and shorter training time in comparison to the conventional
gradient-based learning algorithms. As bigger and diverse datasets become
available, ELM can be quickly retrained as compared to its gradient-based
competitor models. The proposed model achieved a macro average F1-score of 0.95
and the overall sensitivity of ${0.94 \pm 0.02} at a 95% confidence interval.
When compared to state-of-the-art machine learning algorithms, the COV-ELM is
found to outperform its competitors in this three-class classification
scenario. Further, LIME has been integrated with the proposed COV-ELM model to
generate annotated CXR images. The annotations are based on the superpixels
that have contributed to distinguish between the different classes. It was
observed that the superpixels correspond to the regions of the human lungs that
are clinically observed in COVID-19 and Pneumonia cases.
