 The proposed system is a computer-aided diagnostic system for chest x-ray and CT images of several common pulmonary diseases, including COVID-19, viral pneumonia, bacterial pneumonia, tuberculosis, lung opacity, and various types of carcinoma. It uses super-resolution, SR, techniques to enhance image details and deep learning, DL, techniques for both SR reconstruction and classification. The Inception ResNet V2 model is used as a feature extractor in conjunction with a multi-class support vector machine, MCSVM, classifier. The system was tested on three publicly available datasets of CT and x-ray images and achieved a classification accuracy of 98.028%. This system has the potential to serve as a valuable screening tool for lower respiratory disorders and assist clinicians in interpreting chest x-ray and CT images. This article was authored by Hiba M. Emura, Mohamed Arshoeb, Walid El Shafi, and others. We are article.tv, links in the description below.