Classification of chest x-ray samples to detect COVID-19 using Convolutional Neural Networks In early 2020, a new virus began making headlines all over the world, because of the unprecedented speed of its transmission. Its origins have been traced to a food market in Wuhan, China, in December 2019. From there, it’s reached countries as distant as the United States and the Philippines. The virus (officially named SARS-CoV-2) has been responsible for millions of infections globally, causing hundreds of thousands of deaths. India remains the country most affected. The disease caused by an infection with SARS-CoV-2 is called COVID-19, which stands for coronavirus disease 2019. This study aims to use X-Ray sample images from lungs of people infected with covid and the unaffected ones as an input to a Convolution Neural Network. This CNN, after training and validation, can then be used as a model to classify new samples into infected and normal lungs. This study brings with it, a huge scope in the field of medical imaging and can be revolutionary in refined formats, to be used worldwide for better understanding and quick testing of the disease.