The continuous battle against the variants of Corona Virus demands speedy treatment and quick diagnostic reporting on priority basis. With millions of people contracting the infection every day and a mortality rate of 2%, our goal is to solve this growing problem by developing an important and substantive method for diagnosing COVID19 patients. Due to a proportionally reduced number of medical practitioners, testing kits, and other resources in densely populated nations, the exponential development of COVID19 cases is having a significant impact on the health care system, making it increasingly important to identify infected patients. The goal of this work is to develop an exact, productive and time-saving algorithm to identify positive corona patients that addresses the aforementioned issues. In this paper, a Deep Convolution Neural Network model called "EfficientNet"is implemented and explored that can reveal significant diagnostic characteristics to enable radiologists and medical specialists locate COVID-19 infected patients using X-ray pictures of the chest and aid in the fight against the pandemic. The experimental findings conclusively indicate that an accuracy rate of 99.71 percent was obtained for binary classification of Non-COVID and COVID Chest X-ray pictures. Our pretrained Deep Learning classification model can be a significant contribution to recognizing COVID-19 inflicted individuals due to its high diagnostic accuracy. © 2022 IEEE.