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• Classification of normal, viral pneumonia and Covid-19 chest x-ray images. • Proposed a deep convolutional neural network model for early detection of COVID19 cases using chest x-ray images. • Trained the model on a dataset prepared by collecting x-ray images from publically available databases. • Achieved an average accuracy of 97.20% for detecting COVID-19 and an average accuracy of 96.69% for three-class classification (COVID-19 vs. normal vs. viral pneumonia). The results obtained by our proposed model are superior compared to other studies in the literature. • Promising results indicate that this model can be an interesting tool to help radiologists in the diagnosis and early detection of COVID-19 cases. The COVID-19 pandemic is an emerging respiratory infectious disease, also known as coronavirus 2019. It appears in November 2019 in Hubei province (in China), and more specifically in the city of Wuhan, then spreads in the whole world. As the number of cases increases with unprecedented speed, many parts of the world are facing a shortage of resources and testing. Faced with this problem, physicians, scientists and engineers, including specialists in Artificial Intelligence (AI), have encouraged the development of a Deep Learning model to help healthcare professionals to detect COVID-19 from chest X-ray images and to determine the severity of the infection in a very short time, with low cost. In this paper, we propose CVDNet, a Deep Convolutional Neural Network (CNN) model to classify COVID-19 infection from normal and other pneumonia cases using chest X-ray images. The proposed architecture is based on the residual neural network and it is constructed by using two parallel levels with different kernel sizes to capture local and global features of the inputs. This model is trained on a dataset publically available containing a combination of 219 COVID-19, 1341 normal and 1345 viral pneumonia chest x-ray images. The experimental results reveal that our CVDNet. These results represent a promising classification performance on a small dataset which can be further achieve better results with more training data. Overall, our CVDNet model can be an interesting tool to help radiologists in the diagnosis and early detection of COVID-19 cases. [ABSTRACT FROM AUTHOR]