The global COVID-19 pandemic has resulted in significant loss of life and profoundly affected every aspect of human existence. A noteworthy area of study in this crisis is the use of deep learning (DL) models in medical imaging for the treatment of patients with COVID-19. This in-depth research delves into various methods of medical imaging, such as X-rays and computed tomography (CT) images, and their use in DL approaches for differentiating between COVID-19 and pneumonia. The paper outlines how DL techniques, including image localization, segmentation, registration, and classification, can aid in the detection of COVID-19. Recent evaluations have shown InstaCovNet-19 to have a remarkable classification accuracy of 99.80 percent when applied to an Xray dataset of 361 COVID-19 patients, 362 pneumonia patients, and 365 healthy individuals.