Pneumonia is a lethal health ailment, often occurring in middle-aged and elderly patients. This malady is generally identified from critical common cold symptoms. In most test cases, multi imaging techniques are used rather than a single imaging technique to detect pneumonia. This is because varied multiple imaging techniques provide different values for easy analysis and identification of the disease. It is commonly known that pneumonia can be acquired in many ways, the most common being Community Acquired Pneumonia (CAP). The main aim of this research is to analyze and identify the spread of CAP as it occurs mainly in adults above 50 years of age. This is achieved by passing CT images of infected lungs into Pyramid Scene Parsing Network (PSPNet) which is combined with pyramid pooling. This combination trains images to produce many output image subsets based on the number of abnormalities in the image. Complex Daubechies wavelets identify intricate details of the CAP affected areas in infected lungs to produce accurate results as required.