  Background and Objective: During pandemics, the use of artificial
intelligence (AI) approaches combined with biomedical science play a
significant role in reducing the burden on the healthcare systems and
physicians. The rapid increment in cases of COVID-19 has led to an increase in
demand for hospital beds and other medical equipment. However, since medical
facilities are limited, it is recommended to diagnose patients as per the
severity of the infection. Keeping this in mind, we share our research in
detecting COVID-19 as well as assessing its severity using chest-CT scans and
Deep Learning pre-trained models. Dataset: We have collected a total of 1966 CT
Scan images for three different class labels, namely, Non-COVID, Severe COVID,
and Non-Severe COVID, out of which 714 CT images belong to the Non-COVID
category, 713 CT images are for Non-Severe COVID category and 539 CT images are
of Severe COVID category. Methods: All of the images are initially
pre-processed using the Contrast Limited Histogram Equalization (CLAHE)
approach. The pre-processed images are then fed into the VGG-16 network for
extracting features. Finally, the retrieved characteristics are categorized and
the accuracy is evaluated using a support vector machine (SVM) with 10-fold
cross-validation (CV). Result and Conclusion: In our study, we have combined
well-known strategies for pre-processing, feature extraction, and
classification which brings us to a remarkable success rate of disease and its
severity recognition with an accuracy of 96.05% (97.7% for Non-Severe COVID-19
images and 93% for Severe COVID-19 images). Our model can therefore help
radiologists detect COVID-19 and the extent of its severity.
