Pneumonia is an infectious and deadly illness in the respiratory tract that is caused by bacteria, fungi, or a virus that infects the human lung air sacs with the load full of fluid or pus. Chest X-rays are the common method used to diagnose pneumonia and it needs a medical expert to evaluate the result of X-ray. The troublesome method of detecting pneumonia causes a life loss due to improper diagnosis and treatment. With the emerging computer technology, development of an automatic system to detect pneumonia and treat the disease is now possible especially if the patient is in a distant area and medical services are limited. This study intends to incorporate deep learning methods to alleviate the problem. Convolutional Neural Network is optimized to perform the complicated task of detecting diseases like pneumonia to assist medical experts in diagnosis and possible treatment of the disease.The result achieved a 93 percent accuracy rate for CNN with Gamma and the lowest rate is 79.9 %percent achieved by the CNN model. Convolutional neural network (CNNs) has achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the lack of generalizability to previously unseen object classes. Medical datasets are often highly imbalanced with over representation of common medical problems and a paucity of data from rare conditions. We propose simulation of pathology in images to overcome the above limitations.