The WHO has declared Human Coronavirus (HCoV) ongoing outbreak to be a global public health emergency . Corona virus (HCoV) was reported two months ago in Wuhan, China . Health care systems over the world get into a chaotic mode due to limited capacity and a hectic increase of suspected coronavirus cases . The one thing that everybody is trying to do is to reduce the effect of cause created for a patient . This study will show how Machine Learning technique can be used for classifying the infected and healthy lung using the nano scaling imaging technique of computed tomography (CT) lung scans . Pre-processing is used to reduce the effect of intensity variations and for noise removal between CT slices . Then thresholding and other morphological operation is used to separately isolate the background of the CT lung scan . Each dataset that we take undergoes a texture-based feature extraction method in which it uses GLCM along with a wrapper method for optimization . The obtained features are classified using a Deep convolutional neural network, which will classify in several layers . By giving our input of scan images it will train in an efficient manner and gives us an accuracy of 99 %.