  The COVID-19 (coronavirus) is an ongoing pandemic caused by severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus was first identified
in mid-December 2019 in the Hubei province of Wuhan, China and by now has
spread throughout the planet with more than 75.5 million confirmed cases and
more than 1.67 million deaths. With limited number of COVID-19 test kits
available in medical facilities, it is important to develop and implement an
automatic detection system as an alternative diagnosis option for COVID-19
detection that can used on a commercial scale. Chest X-ray is the first imaging
technique that plays an important role in the diagnosis of COVID-19 disease.
Computer vision and deep learning techniques can help in determining COVID-19
virus with Chest X-ray Images. Due to the high availability of large-scale
annotated image datasets, great success has been achieved using convolutional
neural network for image analysis and classification. In this research, we have
proposed a deep convolutional neural network trained on five open access
datasets with binary output: Normal and Covid. The performance of the model is
compared with four pre-trained convolutional neural network-based models
(COVID-Net, ResNet18, ResNet and MobileNet-V2) and it has been seen that the
proposed model provides better accuracy on the validation set as compared to
the other four pre-trained models. This research work provides promising
results which can be further improvise and implement on a commercial scale.
