ABSTRACT
Novel coronavirus (COVID-19) is a hazardous virus. Initially, detected in China and spread worldwide, causing several deaths. Over time, there have been several variants of COVID-19, we have grouped all of them into two major categories. The categories are known to be variants of concern and variants of interest. Talking about the first of these two, it is very dangerous, and we need a system that can not only detect the disease but also classify it without physical interaction with a patient suffering from COVID-19. This paper proposes a Bag-of-Features (BoF) based deep learning framework that can detect as well as classify COVID-19 and all of its variants as well. Initially, the spatial features are extracted with deep convolutional models, while hand-crafted features have been extracted from several hand-crafted descriptors. Both spatial and hand-crafted features are combined to make a feature vector. This feature vector feeds the classifier to classify different variants in respective categories. The experimental results show that the proposed methodology outperforms all the existing methods. © 2021 IEEE.