# A Dual-Stage Vocabulary of Features (VoF)-Based Technique for COVID-19 Variants' Classification
## 1 Introduction  
Coronavirus, known as COVID-19, is a deadly virus that was discovered in Wuhan China in December 2019 and swiftly spread all over the world. It has taken the lives of millions of people. The World Health Organization (WHO) called it a global pandemic [1]. It has several variants; these variants are categorized into three major groups, named as variants of concern, variants of interest, and variants under monitoring. Variants of concern can cause an increase in transmission. These variants are alpha (\(\alpha\)), beta (\(\beta\)), gamma (\(\gamma\)) [2], and delta (\(\delta\)) [3]. On the other hand, variants of concern such as lambda (\(\lambda\)) and mu (\(\mu\)) can cause community transmission or multiple clusters. The epsilon (\(\varepsilon\)), eta (\(\eta\)), iota (\(\iota\)), and kappa (\(\kappa\)) were downgraded to variants of monitoring from variants of concern, while zeta (\(\zeta\)) and theta (\(\theta\)) have not been formally labeled [4]. The variants of concern are more dangerous and cause death. These variants transmit from one individual to others due to physical contact. The most recent and dangerous variant of COVID-19 is the delta variant. Its place of emergence is India. It is highly contagious and has the potential to evade some types of antibodies. Figure 1 shows the variants of concern and their place of emergence. It is necessary to develop a system that detects COVID-19 and classifies its variants without making any physical contact with the infected person. Medical professionals are focused on developing technology to fight this deadly virus. The virus has taken the lives of many people around the world. Artificial intelligence is one of the most promising technologies to detect this virus. A lot of systems have been developed to detect COVID-19 [5]. One of the most widely used approaches to detect COVID-19 from chest X-ray images is the use of a convolutional neural network (CNN) [6].  
Several state-of-the-art techniques using machine learning and deep learning have been proposed for the disease classification and prognostics. In [7], the authors proposed a machine learning framework for the prediction of brain strokes using brain images. Similarly, in [8], a machine learning algorithm was proposed for the prediction of heart diseases based on electrocardiogram (ECG) signals. In [9], the authors introduced a novel framework for the evaluation of the outcome of brain neurons after a stroke. They demonstrated the use of machine learning algorithms for the quantitative evaluation. However, the authors of [10] demonstrated a framework for the features' extraction from electroencephalogram (EEG) signals. The use of machine learning and deep learning proves to be proficient for the detection and classification of several diseases.  
In this paper, we propose a vocabulary of features (VoF)-based deep learning framework to detect COVID-19 and classify its variants. The framework is divided into two sections: the detection of COVID-19 and classification of different variants. Detection is done by performing different operations. After detection, the COVID-19 strain is classified into its variant. For the detection and classification, the vocabulary of features (VoF) is used. These VoF frameworks are used to train models. Several VoFs are used, and the results verify that the proposed method gives the best performance.  
The rest of the paper is organized as follows: Section 2 explains a comprehensive literature review, and Section 3 illustrates the proposed methodology. In Section 4, a comparative analysis of experimental results is presented, while in Section 5, a brief conclusion is drawn.