  With the ever increasing demand for screening millions of prospective "novel
coronavirus" or COVID-19 cases, and due to the emergence of high false
negatives in the commonly used PCR tests, the necessity for probing an
alternative simple screening mechanism of COVID-19 using radiological images
(like chest X-Rays) assumes importance. In this scenario, machine learning (ML)
and deep learning (DL) offer fast, automated, effective strategies to detect
abnormalities and extract key features of the altered lung parenchyma, which
may be related to specific signatures of the COVID-19 virus. However, the
available COVID-19 datasets are inadequate to train deep neural networks.
Therefore, we propose a new concept called domain extension transfer learning
(DETL). We employ DETL, with pre-trained deep convolutional neural network, on
a related large chest X-Ray dataset that is tuned for classifying between four
classes \textit{viz.} $normal$, $pneumonia$, $other\_disease$, and $Covid-19$.
A 5-fold cross validation is performed to estimate the feasibility of using
chest X-Rays to diagnose COVID-19. The initial results show promise, with the
possibility of replication on bigger and more diverse data sets. The overall
accuracy was measured as $90.13\% \pm 0.14$. In order to get an idea about the
COVID-19 detection transparency, we employed the concept of Gradient Class
Activation Map (Grad-CAM) for detecting the regions where the model paid more
attention during the classification. This was found to strongly correlate with
clinical findings, as validated by experts.
