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COVID-19 pandemic spread globally threatens the health and economy. The disease effected the world quickly, the early and fast detection attracted potentially the medical researchers to avoid further outbreak. RT-PCR testing is very time consuming laboratory technique, therefore alternatively radiological imaging techniques are used for fat and accurate diagnosis. Scarcity of exert medical man power, screening of huge volume of population into COVID and normal classes becomes problematic. A novel computer aided technique based on deep learning was proposed in this paper for high rate of screening without the aid of expert radiologists. The pre-trained CNN were trained for dataset-1 with CT-images patch size 16X16 and dataset-2 with patch size 32X32.VGG-16 and GoogLenet with inception V1 were used as pre-trained networks. Data in each set equally distributed between COVID and Non-COVID classes and the data used for training and testing as 75% and 25%. All the four values of true and false of positive and negative classes obtained in the confusion matrix presented high performance on datset-2 in terms of accuracy in measurement, precision, specificity, sensitivity, F1 score and MCC. The experimental results encouraged the use of deep learning in the field of diagnosis of the disease.
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