Over 7 million illnesses and more than 60 lakh deaths have been due to the coronavirus, which was classified as a pandemic by the World Health Organization (WHO). Real-time polymerase chain reaction (RT-PCR) test is the most popular COVID-19 detection method. RT-PCR kits are expensive and require 6 to 9 hours to confirm infection in a patient. A lot of false-negative results are produced by RT-PCR due to its low sensitivity. To identify and diagnose coronavirus, radiological imaging methods such as computed tomography (CT) and chest X-rays are used. Through these radiological images, it is simple to identify some radiological signatures that are shown by COVID-19. Radiologists must study these signs to do this. However, it’s a time consuming job and is prone to error. Therefore, analysis of these radiological images needs to be automated. In this paper, a deep learning model is trained using Convolutional Neural Networks to automate the analysis of chest X-ray images to detect COVID-19. Chest X-rays are chosen over CT scans in this paper. The availability of X-ray machines in most hospitals is the main reason for this. CT scanners are more costly than X-ray devices. In addition, compared to CT scan images, X-rays generate much less ionizing radiation. The dataset used for training the model includes 3616 X-ray images of patients with coronavirus, 1345 X-rays of individuals with viral-pneumonia, and 3616 X-ray scans of healthy individuals. The model has been trained and tested on the images from this dataset and it’s performance is evaluated