  The novel coronavirus universally known as the COVID-19 outbreak arises at
the end of 2019 in one of the East Asian countries and it is subjected to
widespread discussion and debate. There are almost 200 countries affected
across the globe by COVID-19 and it has ruined many lives and the global
economy. The virus is spreading very rapidly at the pace of around 10 fold in
less than a month. Also, in the case of COVID- 19 it is critical to detect the
infection as it employs various symptoms which may differ from person to
person. Hence, diagnosis in starting stage and treatment are very much
important for such type of infectious disease. The chest x-ray is one of the
primary techniques among blood tests and Computed Tomography contributes a
major role in the early diagnosis of COVID-19. There is a rising need for
automated and auxiliary diagnostic tools for early diagnosis, as there are no
accurate and truthful automated tool kits on hand. In this research study, we
have designed a Convolution Neural Network architecture a deep net for the
classification of x-ray images of chest among two classes: COVID-19 or
Non-COVID- 19 infection. The anticipated model is expected to provide accurate
diagnostic results and produced classification accuracy of 99%, 100%, and 100%
with 70%-30%,75%-25% and 80%-20% train-test data split respectively, for the
binary classification of the x-ray image to be COVID-19 or Non-COVID-19
infection category. We have designed the CNN with optimized parameters with 3
convolution layers and optimized number of filters in each layer.
