Nowadays, Covid -19 is one of the major problems in the world. It is spread very quickly by connecting or touching with a covid positive person. To detect the covid - 19, we have to use the testing kits. But we don't have that many kits for testing the covid-19 because the affected number of people is increasing day by day. To solve these big issues, we are introducing one another method. To detect the covid-19 we need to use either chest X-ray's image or Computed Tomography (CT) images. The reason behind to implement of the model is very simple and easy because almost every hospital diagnostic center has X-rays imaging facilities. To identify the covid positive or negative cases, we do not require any kits. In this article, we are introducing one novel model, the process of building the model, and the dataset that we have used to train our model. To train the model we have used almost 1000 chest X-ray images and 700 CT images. For training the model, we are using deep learning algorithms like VGG16, VGG19, Inception V3, RestnetSO, and Xception. We also compare all of the algorithms with some comparison graphs. Among all of the deep learning models, the Inception V3 performs the best accuracy in both datasets.