Novel corona virus pneumonia (COVID-19) is a contagious disease that has already caused thousands of deaths and infected millions of people worldwide. Thus, all technological gadgets that allow the fast detection of COVID-19 infection with high accuracy can offer help to healthcare professionals. This study is proposed to detect COVID-19 based on chest X-ray imaging. In this work, we have proposed an automatic prediction of COVID-19 using a deep convolution neural network based pre-trained transfer models and chest X-ray images. For this purpose, we have used ConvNet, AlexNet and DenseNet pre-trained models to obtain higher prediction accuracies for different X-ray images of normal (healthy) and COVID-19 patients. The classification method uses transfer learning method for the purpose of optimizing hyper-parameter values within the transfer learning tuning of a CNN. The trained model is then used to classify a set of X-ray images, upon which the qualitative explanations are performed. The presented approach was tested on a collection of 116 COVID and 317 normal X-ray images to achieve high classification accuracy i.e., 92%. The achieved high classification accuracy enabled us to perform a qualitative in-depth analysis, which revealed that there are some regions of greater importance when identifying COVID19 cases.