The COVID-19 pandemic keeps on devastatingly affecting the wellbeing and prosperity of the worldwide populace. To reduce the rapid spread of the COVID-19 virus primary screening of the infected patient repeatedly is a need. Medical imaging is an essential tool for faster diagnosis to fight against the virus. Early diagnosis on chest radiography shows the Coronavirus disease (COVID-19) infected images shows variations from the Normal images. Deep Convolution Neural Networks shows an outstanding performance in the medical image analysis of Computed Tomography (CT) and Chest XRay (CXR) images. Therefore, in this paper, we designed a Deep Convolution Neural Network that detects COVID-19 infected samples from Pneumonia and Normal Chest XRay (CXR) images. We also construct the dataset that contains 6023 CXR images in which 5368 images are used for training and 655 images are used for testing the model for the three categories such as COVID-19, Normal, and Pneumonia. The proposed model shows outstanding performance with 97.74% accuracy and 96% average F-Score. The results prove that the model can be used for preliminary screening of the COVID-19 infection using radiological Chest X-Ray (CXR) images to accelerate the treatment for the patients under investigation (PUI) who need it most.