The purpose of this experiment was to find out if deep learning models accurately diagnose patients with pneumonia. If deep learning models were used with Convolution Neural Network layers and pooling techniques, then the efficiency of detection would increase with max pooling technique and the most amount of CNN layers. If deep learning models were used with Convolution Neural Network layers, then the amount of time the model takes to run would increase with the increase of CNN layers. A model was created with three convolution neural network (CNN) layers and max pooling technique. The number of CNN layers was increased and the pooling technique changed to average pooling technique. The models were trained, validated, and tested using normal and pneumonia x-ray images. Overall, the experiment supported the hypothesis. For both pooling techniques, the increase of CNN layers increased the accuracy and the time it took for the model to run. In addition, the most layers and max pooling lead to a higher accuracy of around 74 percent while most layers and average pooling lead to an accuracy of around 49 percent. Different deep learning models using max pooling could be applied to other lung conditions where patients come in with bronchitis or pulmonary edema. Validated models could also be used in other areas of the body to help diagnose patients that have pain.