The novel coronavirus (covid-19) with a starting point Wuhan, China has spread rapidly amongst countries all around the globe. There are approximately 22 Million confirmed cases as of August 2020. Compared to the daily rises in cases, there are few covid-19 testing kits available. There is a need for testing kits and some countries and regions are in much more need than others. Due to the lack of testing kits available to the public, it is necessary to implement a detection system as an alternative diagnosis method for covid-19. With rapid advancements in medical image processing techniques, the development of intelligent prediction and diagnosis tools have also increased at a rapid rate. Machine learning techniques are widely accepted as a prominent tool to improve the prediction and diagnosis of many illnesses such as cancer. In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Cd-19 disease, and normal incidents is utilized for the automatic detection of the Coronavirus disease. The procedure of transfer learning was adopted using three different computer vision models that were pre-trained on thousands of images from ImageNet. The models used for this specific purpose are VGG16, VGG19 and ResNet101. The dataset was generated by gathering different classes of images and combining them to form a dataset of size 1GB for better accuracy.