This paper investigates the use of artificial intelligence (AI) in rapidly and accurately detecting COVID-19 pneumonia from chest X-ray images. The authors created a large database containing 423 COVID-19, 1485 viral pneumonia, and 1579 normal chest X-ray images. They employed transfer learning techniques and image augmentation to train and validate several pre-trained deep Convolutional Neural Networks (CNNs). The networks were trained to classify images into two schemes: normal vs. COVID-19 pneumonia, and normal, viral, and COVID-19 pneumonia with and without image augmentation. The results showed high accuracy, precision, sensitivity, and specificity for both schemes, with values ranging from 97.9% to 99.7%. The study highlights the potential of AI in improving the speed and accuracy of COVID-19 diagnosis, which is crucial during the pandemic when resources are limited.This paper investigates the use of artificial intelligence (AI) in rapidly and accurately detecting COVID-19 pneumonia from chest X-ray images. The authors created a large database containing 423 COVID-19, 1485 viral pneumonia, and 1579 normal chest X-ray images. They employed transfer learning techniques and image augmentation to train and validate several pre-trained deep Convolutional Neural Networks (CNNs). The networks were trained to classify images into two schemes: normal vs. COVID-19 pneumonia, and normal, viral, and COVID-19 pneumonia with and without image augmentation. The results showed high accuracy, precision, sensitivity, and specificity for both schemes, with values ranging from 97.9% to 99.7%. The study highlights the potential of AI in improving the speed and accuracy of COVID-19 diagnosis, which is crucial during the pandemic when resources are limited.