 Pneumonia is a serious lung condition caused by either bacteria or virus. Early detection is essential to prevent further complications and reduce mortality rates. To address this issue, researchers developed a deep convolutional neural network, CNN, based approach to accurately identify pneumonia from digital x-ray images. The CNN was trained with 5,247 chest x-ray images consisting of normal, bacterial, and viral cases. Three classification schemes were employed to differentiate between normal, bacterial, and viral cases. The results showed that the proposed model achieved high accuracy of 98% for normal pneumonia cases, 95% for bacterial and viral cases, and 93.3% for all three cases combined. This is the highest accuracy reported in the literature for the given tasks. Thus, the proposed model can be used to quickly diagnose pneumonia and assist in airport screenings. This article was authored by Tosafur Rahman, Muhammad E. H. Chowdhury, Amit Khandekar, and others.