<p>The global spread of the Coronavirus has caused a disastrous effect, affecting millions of people and making it crucial to take action. Numerous experts have worked extensively to create viable vaccines in the fight against this infectious disease. The current study offers hope by suggesting a deep learning model, Deep CP-CXR, for determining patients with Covid-19 and pneumonia. Our study encompasses two significant investigations. First, we used images from chest X-rays for binary classification to distinguish Covid-19-diagnosed patients from normal patients. Second, using chest X-ray images, we expanded the study to include several groups, such as pneumonia, Covid-19, and normal instances. The results of our studies were extremely promising. The binary classification achieved a remarkable average accuracy of 100%, allowing for accurate classification between Covid-19 patients and normal cases. In addition, the multiple-category classification was able to distinguish between Covid-19, pneumonia, and normal individuals with a remarkable average accuracy of 98.57%. These astounding findings lead us to conclude that the Deep CP-CXR method we’ve suggested for classifying Covid-19 and pneumonia patients enables medical professionals to perform it accurately. Healthcare providers worldwide will benefit significantly from this development since it has the potential to enhance both the detection and treatment of these ailments. The proposed deep learning approach improves the speed and precision in classifying the disease with which doctors can diagnose and treat their patients effectively.</p>