The novel discovered disease coronavirus popularly known as COVID19 is a lung infection disease that causes adverse effects on the human respiratory system. It is caused due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and declared a pandemic by the World Health Organization (WHO). For COVID-19 detection, chest radiography, i.e., computerized tomography(CT) scan, X-rays, etc. are widely investigated. In the proposed work, a deep learning model, i.e., truncated VGG16(Visual Geometry Group from Oxford) is implemented to screen COVID-19 CT scans. The VGG16 architecture is fine-tuned and used to extract features from CT Scan images. Further Principal Component Analysis (PCA) is used for feature selection. The final classification is performed using four different classifiers, namely deep convolutional neural network(CNN) , Extreme Learning Machine (ELM), Online sequential ELM, and Bagging Ensemble with support vector machine (SVM) . The best performing classifier Bagging Ensemble with SVM within 385 ms achieved an accuracy of 95.7%, precision of 95.8%, Area Under Curve (AUC) of 0.958, and an F1 score of 95.3% on 208 test images. The results obtained on diverse datasets prove the superiority and robustness of the proposed work in comparison to the techniques available in the literature.