# A Dual-Stage Vocabulary of Features (VoF)-Based Technique for COVID-19 Variants' Classification
## 2 Literature Review  
Coronavirus is a highly dangerous virus. There have been a lot of systems used to detect COVID-19. The analysis of radiology images assisting in detecting coronavirus has obtained elevated attention by researchers around the world. In [11], a deep convolutional neural network-based technique is proposed to detect the virus. The authors fused two datasets and made a combined dataset. The accuracy achieved by the model was 98.7%. Another model used for classification is known as the support vector machine (SVM). The researcher studied different neural networks and among them, ResNet-50 emerged as the best, having an accuracy of 95%. However, this approach is complex and requires a massive dataset as well as abundant execution time [12]. Furthermore, [PERSON] et al. suggested three  
Figure 1: Variants of concern and their origin. pre-trained CNN architectures consisting of Inceptionv3, Inception-ResNet-v2, and ResNet-50, which help in detecting COVID-19-positive cases from X-rays. It was observed that a 98% accuracy was achieved for ResNet50, whereas an 87% accuracy was obtained for Inception-ResNet-v2, and Inceptionv3 gained an accuracy of 97%. The drawback of all these models is that they took only 100 images for examination. For a large dataset, the performance of the model will decline slowly [12]. Another method is proposed for the detection of COVID-19, named Covid-Net, helped in classifying several classes of illness: COVID-19, severe pneumonia, and normal. However, this approach gained only 92.4% accuracy, which is not much better as compared to other techniques [13]. Another article used a SqueezeNet model and achieved an accuracy of 98.3%. However, this model is not recommended as it required high processing power and speed for its working [14]. Many techniques have been in discussion for the detection of coronavirus pneumonia, among which some of them use deep learning techniques on computed tomography (CT) images, which can detect up to 89.5% for the accuracy, 87% for the sensitivity, and 88% for the specificity [15]. An advanced deep learning technique that automatically detects COVID-19 was produced by using X-ray images and DCNN-based Inception V3 model, which can validate the accuracy up to 98% [16]. In [17], COVID-19 was detected using X-ray images, and for those images, three algorithms were used and a 95%-99% F-score could be deduced successfully from them. In [18], an exemplar model evolved, which first applies a fuzzy tree transformation and then uses a multi-Kernel local binary pattern. Then, feature detection from the images is done and afterward, they are processed through two or three algorithms like the support vector machine (SVM) and decision tree. SVM produced the best results with an accuracy of about 97.01%. Similarly, in paper [19], a model to detect COVID-19 with a CNN model called CoroDet is proposed. This used the raw images of CT scan and X-rays. This model is mainly used for three different types of classifications, among which the accuracy achieved is 94.2%, 99.1%, and 91.2% for the three-class classification, two-class classification, and four-class classification, respectively. In [19], a model established on deep learning is introduced whose architecture relies on the ResNet-101 CNN network and recognizes objects from millions of images and then detect anomalies in chest X-ray images. The accuracy was found to be about 71.9%. In [20], another automatic model for COVID-19 is presented using X-ray images. These are classified as binary and multi-class classifications. A real-time object detection system was used, named DarkNet, which uses 17 convolutional layers. The accuracy achieved was 87.02% and 98.08% for multi-class and binary classifications, respectively. [PERSON] et al. proposed a novel deep learning-based technique for the detection of COVID-19 from chest X-rays and CT scans. They achieved the accuracy of 82.94% and 93.94% for chest X-rays and CT scans, respectively [21]. CT scans can also be used for the detection of coronavirus.