The COVID-19 virus induces infection in both the upper respiratory tract and the lungs. Chest X-ray are widely used to diagnose various lung diseases. Considering chest X-ray and CT images, we explore deep-learning-based models namely: AlexNet, VGG16, VGG19, Resnet50, and Resnet101v2 to classify images representing COVID-19 infection and normal health situation. We analyze and present the impact of transfer learning, normalization, resizing, augmentation, and shuffling on the performance of these models. We explored the vision transformer (ViT) model to classify the CXR images. The ViT model incorporates multi-headed attention to disclose more global information in constrast to CNN models at lower layers. This mechanism leads to quantitatively diverse features. The ViT model renders consolidated intermediate representations considering the training data. For experimental analysis, we use two standard datasets and exploit performance metrics: accuracy, precision, recall, and F1-score. The ViT model, driven by self-attention mechanism and longrange context learning, outperforms other models.