Artificial intelligence is perceived as a possible solution to problems requiring highly trained human expertise, such as in healthcare. The idea is to automate tedious tasks related to the detection of health issues, as well as pave the way to understanding new complex issues to which humanity has not yet given an answer. Pneumonia is a respiratory infection caused by bacteria or viruses; it affects many individuals, especially in developing and underdeveloped nations, where high levels of pollution, unhygienic living conditions, and overcrowding are relatively common, together with inadequate medical infrastructure. Pneumonia causes pleural effusion, a condition in which fluids fill the lung, causing respiratory difficulty. Early diagnosis of pneumonia is crucial to ensure curative treatment and increase survival rates. Chest X-ray imaging is the most frequently used method for diagnosing pneumonia. However, the examination of chest X-rays is a challenging task and is prone to subjective variability. In this study, we developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We employed deep transfer learning to handle the scarcity of available data and two pre-trained convolutional neural network models: ResNet-50, and DenseNet-121. The results from the system evaluation indicate that the system has high performance and accuracy levels.