# A Dual-Stage Vocabulary of Features (VoF)-Based Technique for COVID-19 Variants' Classification
## 4 Experimental Results
### Simulation Results  
The performance of the trained model was evaluated. The training accuracy and loss of the model were 99.95% and 1.02%, respectively. First, the performance of first stage is presented. Tables 2 and 3 show the performance of different DCNNs applied for the detection of COVID-19 from chest X-rays and CT scans, respectively. The highest accuracy achieved by the proposed model for first stage was 99.5% and 99.74% for CT scans and chest X-rays, respectively. The confusion matrices of the proposed framework for chest X-rays and CT scans are shown in Figure 8. The ROC curve for the training and testing of the first stage of the proposed methodology is shown in Figure 9. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name of DCNN** & **Accuracy** & **Specificity** & **Sensitivity** & **F1-Score** & **Cohen’s Kappa (x)** &
\begin{tabular}{c} **Classification** \\ **Error** \\ \end{tabular} \\ \hline AlexNet [29] & 96.4\% & 93.5\% & 92.5\% & 93.00\% & 0.91 & 3.6\% \\ \hline ResNet-50 [30] & 97.5\% & 94.6\% & 93.6\% & 94.10\% & 0.92 & 2.5\% \\ \hline Inception v3 [31] & 98.5\% & 95.6\% & 94.6\% & 95.10\% & 0.93 & 1.5\% \\ \hline DarkNet-53 [32] & 96.2\% & 93.3\% & 92.3\% & 92.80\% & 0.90 & 3.8\% \\ \hline EfficientNet-b7 [33] & 98.9\% & 96.0\% & 95.0\% & 95.50\% & 0.93 & 1.1\% \\ \hline GoogLeNet [34] & 96.4\% & 97.4\% & 95.4\% & 96.39\% & 0.94 & 3.6\% \\ \hline Inception ResNet v2 [35] & 98.7\% & 96.5\% & 97.1\% & 96.80\% & 0.95 & 1.3\% \\ \hline MobileNet v2 [36] & 98.4\% & 96.3\% & 99.1\% & 97.68\% & 0.95 & 1.6\% \\ \hline SqueezeNet [37] & 89.1\% & 90.4\% & 93.3\% & 91.83\% & 0.89 & 10.9\% \\ \hline ShuffleNet [38] & 99.1\% & 99.1\% & 100\% & 99.55\% & 0.97 & 0.9\% \\ \hline VGG-19 [39] & 92.2\% & 94.6\% & 96.1\% & 95.34\% & 0.93 & 7.8\% \\ \hline Xception [40] & 98.5\% & 99.2\% & 100\% & 99.60\% & 0.98 & 1.5\% \\ \hline
**Proposed** & **99.74\%** & **99.6\%** & **100\%** & **99.80\%** & **0.99** & **0.3\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Testing performance of different DCNNs for the chest X-ray dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name of DCNN** & **Accuracy** & **Specificity** & **Sensitivity** & **F1-Score** & **Cohen’s Kappa (x)** &
\begin{tabular}{c} **Classification** \\ **Error** \\ \end{tabular} \\ \hline AlexNet [29] & 94.1\% & 93.2\% & 95.3\% & 94.24\% & 0.92 & 5.9\% \\ \hline ResNet-50 [30] & 95.2\% & 95.6\% & 94.6\% & 95.10\% & 0.93 & 4.8\% \\ \hline Inception v3 [31] & 96.2\% & 94.6\% & 96.1\% & 95.34\% & 0.93 & 3.8\% \\ \hline DarkNet-53 [32] & 93.9\% & 96.3\% & 99.1\% & 97.68\% & 0.95 & 6.1\% \\ \hline EfficientNet-b7 [33] & 96.6\% & 99.2\% & 100\% & 99.60\% & 0.98 & 3.4\% \\ \hline GoogLeNet [34] & 94.1\% & 93.5\% & 92.5\% & 93.00\% & 0.91 & 5.9\% \\ \hline Inception ResNet v2 [35] & 96.4\% & 93.3\% & 92.3\% & 92.80\% & 0.90 & 3.6\% \\ \hline MobileNet v2 [36] & 96.1\% & 96.5\% & 97.1\% & 96.80\% & 0.95 & 3.9\% \\ \hline SqueezeNet [37] & 86.8\% & 90.4\% & 93.3\% & 91.83\% & 0.89 & 13.2 \\ \hline ShuffleNet [38] & 96.8\% & 97.4\% & 95.4\% & 96.39\% & 0.94 & 3.2\% \\ \hline VGG-19 [39] & 89.9\% & 90.4\% & 93.3\% & 91.83\% & 0.90 & 10.1\% \\ \hline Xception [40] & 96.2\% & 99.1\% & 100\% & 99.55\% & 0.97 & 3.8\% \\ \hline
**Proposed** & **99.5\%** & **99.7\%** & **99.3\%** & **99.50\%** & **0.99** & **0.6\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Testing performance of different DCNNs for the CT scan dataset. considered for training the model. The comparison of all variants in terms of handcrafted features in X-ray images is shown in Figure 12.  
The classification results of the X-rays and CT scans are shown in Figure 13 in the form of confusion matrices. The accuracy for X-ray images was 99.12%, while for CT scan it was 98.54%. Figure 11: Comparison of the first-stage accuracy of the proposed method with others using CT scan images. Figure 10: Comparison of the first-stage accuracy of the proposed method with others using chest X-rays.