## 3 Results and Discussion
### Investigation on Classification Performance
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Test Type** & \begin{tabular}{c} **ML/DL** \\ **Techniques** \\ \end{tabular} & \
\begin{tabular}{c} **Prediction** \\ **Results** \\ \end{tabular} & **Country** &
\begin{tabular}{c} **Cited by No of** \\ **Papers** \\ \end{tabular} \\ \hline
[40] & CT Images & \
\begin{tabular}{c} 3-D CNN \\ ResNet-18 \\ \end{tabular} &
\begin{tabular}{c} Overall Accuracy \\ 86.7\% \\ \end{tabular} & China & 443 \\ \hline
[3] & X-ray Images & \
\begin{tabular}{c} CNN, MobileNet \\ v2 \\ \end{tabular} &
\begin{tabular}{c} Accuracy of 96.78\% \\ Sensitive 98.66\% \\ Specificity 96.46\% \\ \end{tabular} & Greece & 480 \\ \hline
[45] & X-ray Images & \
\begin{tabular}{c} D.N.N., VGG-19 \\ ResNet-50, \\ COVID-Net \\ \end{tabular} &
\begin{tabular}{c} Accuracy of \\ 93.3\% \\ \end{tabular} & Canada & 558 \\ \hline
[46] & X-ray Images & \
\begin{tabular}{c} CNN, RestNet50 + \\ SVM \\ \end{tabular} &
\begin{tabular}{c} Accuracy of \\ 95.33\% \\ \end{tabular} & India & 250 \\ \hline
[47] & X-ray Images & \
\begin{tabular}{c} D-CNN, VGG19, \\ DenseNet201 \\ \end{tabular} &
\begin{tabular}{c} Classification with \\ F1-scores of 0.89 and 0.91 \\ 0.91 \\ \end{tabular} & Egypt & 251 \\ \hline
[68] & X-ray Images &
\begin{tabular}{c} CNN, \\ COVID-ResNet, \\ COVID-5 CNet \\ \end{tabular} & Accuracy of 97.61\% & Turkey & 1 \\ \hline
[48] & X-ray Images & \
\begin{tabular}{c} ResNet50, \\ DenseNet201, \\ Inception-v3, and \\ Xception \\ \end{tabular} &
\begin{tabular}{c} AUC 0.996, \\ Overall sensitivity of 0.94 \\ \end{tabular} & USA & 2 \\ \hline
[51] & X-ray Images &
\begin{tabular}{c} CNN, \\ ResNet50V2 \\ \end{tabular} & Accuracy of 89.92\% & UK & 104 \\ \hline
[55] & X-ray Images &
\begin{tabular}{c} DCNN, CheXNet + \\ DenseNet-201, \\ Inception-v3, and \\ Xception \\ \end{tabular} & Accuracy of 99.7\% \\
[55] & X-ray Images & \
\begin{tabular}{c} DCNN, CheXNet + \\ DenseNet-201 \\ \end{tabular} &
\begin{tabular}{c} Precision, sensitivity of \\ 99.7\% and specificity is \\ 99.55\% \\ \end{tabular} & Qatar & 161 \\ \hline
[57] & X-ray Images & \
\begin{tabular}{c} CNN, \\ Classification \\ Grad-CAM \\ \end{tabular} &
\begin{tabular}{c} AUC 95.13\%, the \\ sensitivity of 90\%, and \\ specificity of 87.84\% \\ \end{tabular} & China & 161 \\ \hline
[64] & X-ray Images &
\begin{tabular}{c} CNN, \\ COVID-ResNet \\ \end{tabular} & Accuracy of 96.23\% & USA & 125 \\ \hline
[65] & X-ray Images & D-CNN, DeTraC &
\begin{tabular}{c} Accuracy of 95.12\% \\ Sensitivity of 97.91\% \\ and Specificity of 91.87\% \\ \end{tabular} & Egypt & 161 \\ \hline
[66] & X-ray Images & \
\begin{tabular}{c} D.N.N., Deep \\ COVID Explainer \\ \end{tabular} &
\begin{tabular}{c} Positive Predictive Value \\ 96.12\% and recall of \\ 94.3\% \\ \end{tabular} & Germany & 34 \\ \hline
[67] & X-ray Images &
\begin{tabular}{c} VGG-16 \\ VGG-19 \\ \end{tabular} & Accuracy of 87.49\% & Australia & 5 \\ \hline
[38] & X-ray Images &
\begin{tabular}{c} CNN, AlexNet, \\ GoogleNet, \\ SqueezeNet \\ \end{tabular} & Overall accuracy 99\% & Saudi Arabia & 4 \\ \hline
[69] & X-ray Images &
\begin{tabular}{c} Classification \\ Models \\ K.N.N., ANN, D.T., \\ SVM, \\ \end{tabular} & Overall accuracy of 93.41\% & India & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._In RT-PCR, samples collected from a person's body, such as nose or throat, are investigated to detect the presence of the virus. The reliability of RT-PCR is not suitable owing to a high false rate, due to which it cannot give accurate results [73]. The availability of RT-PCR is relatively short around the world. Moreover, it includes a lengthy procedure for detection and is quite expensive. RT-PCR based model revealed with the least accuracy of 80% in recent research [59] and the maximum A.U.C. of 0.84 for another analysis [58]. The clinical blood test-based model was found with the lowest accuracy of 84.24% in the previous