The introduction of COVID-19 has negatively impacted both global health and human health. The main tactic to limit the spread of this virus is the early and accurate identification of the viral infection. Real-Time Polymerase Chain Reaction is the most commonly used method for determining Covid-19 (RT-PCR). However, RT-PCR tests take a long time and may produce false results. Therefore, radiology imaging techniques (like X-ray & CT scan pictures) can aid in determining Covid-19 since these images provide essential information about the sickness caused by the Covid-19 virus. But, reviewing each report requires a lot of time and multiple radiology experts, which is a challenging task during the pandemic. So, a model that can automatically detect Covid-19 both X-ray and CT scan photographs of the chest is developed using a Convolutional Neural Network (CNN) based encounter. The primary goal of this automated detection is to deliver faster and more accurate results. However, a chest X-ray is used in this paper instead of CT scan. This is due to the fact that most hospitals have X-ray equipment. Even, the X-ray machine is less expensive than a CT scanner. And when compared to CT scans, X-rays expose people to less ionizing radiation. The model is tested against random samples to get the results. Finally, various performance metrics will be used to evaluate the model's performance.