Mysterious sickness with flu-like signs became first located in Wuhan town of China. This sickness became resulting from extreme acute respiration syndrome coronavirus 2 (SARSCoV-2). Covid-19 introduced havoc internationally affecting each public fitness and the economy globally. And inflicting the biggest worldwide recession because of the Great Depression. With the fundamental replica variety (R0) ranging from 2-2.5, it's far vital to become aware of the effective instances and deal with them. There is a demand for auxiliary diagnostic tools. The information amassed the usage of the radiology imaging strategies offers exceptional data about the COVID-19 virus. Radiological photographs and superior synthetic intelligence(AI) strategies can work withinside the choice of a brief analysis of the infection. In this we examine, an automated version for COVID-19 detection the usage of chest X-ray photographs is rendered. The rendered version is advanced to cater stable diagnostics for binary category and multi-class category. Our version gave a category accuracy of 98.08 percentage for binary classes and 87.02 percentage for multi-magnificence instances. In our investigation, the Darknet version was used as a classifier for a YOLO (you only look once) real-time object identification system. We are using VGG-sixteen architecture and added filtering on every layer. Our version can be engaged to support radiologists withinside the preliminary screening, and also can be hired through cloud to immediately ​screen patients.