  Coronavirus disease (COVID-19) is a pandemic disease, which has already
caused thousands of causalities and infected several millions of people
worldwide. Any technological tool enabling rapid screening of the COVID-19
infection with high accuracy can be crucially helpful to healthcare
professionals. The main clinical tool currently in use for the diagnosis of
COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which
is expensive, less-sensitive and requires specialized medical personnel. X-ray
imaging is an easily accessible tool that can be an excellent alternative in
the COVID-19 diagnosis. This research was taken to investigate the utility of
artificial intelligence (AI) in the rapid and accurate detection of COVID-19
from chest X-ray images. The aim of this paper is to propose a robust technique
for automatic detection of COVID-19 pneumonia from digital chest X-ray images
applying pre-trained deep-learning algorithms while maximizing the detection
accuracy. A public database was created by the authors combining several public
databases and also by collecting images from recently published articles. The
database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579
normal chest X-ray images. Transfer learning technique was used with the help
of image augmentation to train and validate several pre-trained deep
Convolutional Neural Networks (CNNs). The networks were trained to classify two
different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and
COVID-19 pneumonia with and without image augmentation. The classification
accuracy, precision, sensitivity, and specificity for both the schemes were
99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%,
respectively.
