## 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
[26] & CT Images & CNN, COVNet & AUC 0.96 & China & 553 \\ \hline
[39] & CT Images & \
\begin{tabular}{c} CNNs, ResNet-101 \\ \& Xception \\ \end{tabular} &
\begin{tabular}{c} AUC of 0.99 \\ Sensitivity 98.02\% \\ Specificity 99.51\% \\ \end{tabular} & Iran & 120 \\ \hline
[41] & CT Images & \
\begin{tabular}{c} 3-D DNN, \\ DeCoVNet \\ \end{tabular} &
\begin{tabular}{c} Accuracy \\ 0.90 \\ \end{tabular} & China & 205 \\ \hline
[42] & C.T. Images & \
\begin{tabular}{c} Inception Transfer \\ learning model \\ establish the \\ algorithm \\ \end{tabular} &
\begin{tabular}{c} Accuracy of 89.5\% with \\ Specificity of 0.88 and \\ Sensitivity of 0.87 \\ \end{tabular} & China & 376 \\ \hline
[43] & C.T. Images & \
\begin{tabular}{c} D.N.N., \\ DRE-Net \\ \end{tabular} &
\begin{tabular}{c} A.U.C. of 0.99 \\ Sensitivity of 0.93 \\ \end{tabular} & China & 198 \\ \hline
[49] & CT Images & \
\begin{tabular}{c} 2D and 3D deep \\ learning \\ (Resnet-50-2D) and \\ AI Models \\ \end{tabular} &
\begin{tabular}{c} AUC of 0.99 \\ Sensitive 92.2\% \\ Specificity 92.2\% \\ \end{tabular} & China & 306 \\ \hline
[50] & C.T. Images &
\begin{tabular}{c} Classification \\ Stage 1 SVM, Stage 2 GLCM, GLSZ \\ MDWT \\ \end{tabular} & Accuracy of 99.68\% & Turkey & 103 \\ \hline
[52] & C.T. Images &
\begin{tabular}{c} Multilayer \\ perceptron and \\ LSTM \\ \end{tabular} & AUC of 0.954 & China & 48 \\ \hline
[54] & CT Images & \
\begin{tabular}{c} Combined model \\ 3D UNet++ and \\ RestNet-50 \\ \end{tabular} &
\begin{tabular}{c} AUC of 0.991 \\ Sensitivity of 0.974 and \\ specificity of 0.922 \\ \end{tabular} & China & 99 \\ \hline
[60] & C.T. Images & \
\begin{tabular}{c} 3D-DNN, \\ COVID-19 Net \\ \end{tabular} &
\begin{tabular}{c} AUC 0.86 Sensitivity of \\ 79.35\% and specificity of \\ 71.43\% \\ \end{tabular} & China & 95 \\ \hline
[61] & C.T. Images &
\begin{tabular}{c} CNN, Multi-task \\ learning, \\ self-supervised \\ learning, \\ DenseNet-169 \\ \end{tabular} & Accuracy of 0.89 and \\ \hline
[44] & C.T. Images &
\begin{tabular}{c} Five classifiers, \\ Decision tree, \\ k-nearest neighbor, \\ naive Bayes, \\ support vector \\ machine, Proposed \\ COVIDing model \\ \end{tabular} & Accuracy of 91.4\% \\
[44] & C.T. Images &
\begin{tabular}{c} Naive Bayes, \\ support vector \\ machine, Proposed \\ COVIDing model \\ \end{tabular} & Iran & 7 \\ \hline
[36] & X-ray Images & CNN, CoroNet &
\begin{tabular}{c} Overall Accuracy \\ 89.6\% \\ \end{tabular} & India & 159 \\ \hline
[37] & X-ray Images & \
\begin{tabular}{c} CNN \\ VGG16 \\ \end{tabular} &
\begin{tabular}{c} Average accuracy \\ 0.97\% \\ \end{tabular} & Italy & 69 \\ \hline
[11] & X-ray Images &
\begin{tabular}{c} CNN \\ DarkCovidNet \\ \end{tabular} & Accuracy of 98.08\% & Turkey & 417 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of state-of-art COVID-19 prediction algorithms.