As a result of the COVID-19 pandemic, the populace has seen one of the most drastic periods in human history, and the ailment tends to grow fast across the worldwide. It is believed that the disease primarily causes respiratory issues in humans. Recognising this disease has also tend become a difficult activity due to the fact that both diseases have comparable effects on the lungs. It is believed that pneumonia is an infection raised by bacteria in parts of the alveoli in lungs that, if left untreated, can result in death. Thus, the development of an automated method to diagnose the sickness could be advantageous to the human species. As a result of the ongoing growth of these areas of research, it has been seen that the basics of deep learning and machine learning continue to contribute to the execution and examination of healthcare based images and the performance classification of the same. In this study, we evaluate the CNN model and a CNN based encapsulation featured capsule network for predicting impacted and unaffected chest X-ray images patients.