Deep learning has grown rapidly in recent years, with excellent results for many computer vision applications, such as image classification and object detection. One aspect of the increased popularity of deep learning is its ability to mitigate the need for handcrafted features. It reduces the manual work of identity recognition and facilitates automatic processing. Inspired by the advantages of the hierarchical feature extraction of deep learning, this work investigates the development of a Convolutional Neural Network (CNN) algorithm to solve the problem of Covid 19 detection based on deep learning techniques. The proposed system takes raw grey scale x-ray images as input and classifies the images to Covid-19 or non-Covid-19. The data consists of grey scale chest x-ray images of normal people, Covid-19 patients, and other images for pneumonia and normal patients. We started with an extensive literature review of the latest Artificial Intelligence and computer vision techniques for Covid-19 detection. The data is available from open source. Deep transfer learning using pre-trained CNN – such as VGG-16, VGG-19, RESNET-50, Inception V3 and Exception – were used. The results obtained were promising for most of networks. Some layers were added to the pretrained networks to improve performance. Multiple design iterations were conducted to improve the model's performance. The best design model achieved better performance compared to state-of-the-art pre-trained models using same data. Indeed, after a training phase of less than 100 epochs, our model utilizing VGG19 network was capable of providing 87.08% accuracy, 87.50% Precision, 87.00% Recall and 87.25 F1. The most challenge of this work was data sample number and this challenge is due to non-availability of reliable data.