## 3 Results and Discussion
### Investigation on Classification Performance
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Test Type**} & **ML/DL** & **Prediction** & **Country** & **Cited by No of** \\  & & **Techniques** & **Results** & **Points** & **Papers** \\ \hline \multirow{4}{*}{[35]} & \multirow{4}{*}{Chest X-ray CT Images} & CNN, RestNet50, & & & & \\  & Chest X-ray & Inception V3, & & & & \\  & CT Images & Inception- & & & & \\  & & RestNetV2 & & & & \\ \hline \multirow{4}{*}{[53]} & Chest X-ray CT Images & D-CNN, A.I. & A.U.C. of 97.91\% & & & \\  & CT Images & system & & The sensitivity of 90.19\% & & \\  & & & and specificity of 95.76\% & & & \\ \hline \multirow{4}{*}{[56]} & \multirow{4}{*}{Chest X-ray CT Images} & Simple 2-D CNN & & & & \\  & Chest X-ray & Model and & Accuracy of 98\% on & & & \\  & & pre-trained & X-ray images and 94.1\% & Iraq & 80 \\  & & AlexNet with & on C.T. images & & & \\  & & transfer learning & & & & \\ \hline \multirow{4}{*}{[62]} & Chest X-ray CT Images & D-CNN, Resnet50, & & & & \\  & Chest X-ray & MobileNet_V2, & & & & \\  & CT Images & Inception\_Resnet\_V2 & & Accuracy of 96.61\% & Morocco & 39 \\ \hline \multirow{4}{*}{[58]} & \multirow{4}{*}{RT-PCR} & Support Vector & & & & \\  & Machine, Random Forests, Neural & AUC 0.847, Sensitivity & & & \\  & Networks, Logistic & 0.67, Specificity 0.85 & & Brazil & 31 \\  & & Regression & & & \\ \hline \multirow{4}{*}{[59]} & \multirow{4}{*}{RT-PCR} & Support Vector & & & \\  & Machine, K.N.N., & Decision Tree, & & & \\  & Random Forest & & Accuracy of 80\% & China & 147 \\ \hline \multirow{4}{*}{[70]} & \multirow{4}{*}{Clinical Blood Test} & Random Forests & Accuracy of 0.9512, & & & \\  & & (R.F.) & Sensitivity 0.9697, and & & \\  & & Specificity 0.9595 & & & \\ \hline \multirow{4}{*}{[71]} & \multirow{4}{*}{Clinical Blood Test} & CRISP-DM, & & & & \\  & & Random Forest, & & & \\  & Deep Neural & A.U.C. of 0.97, & & & \\  & Network, & Sensitivity 81.9\%, and & & & \\  & Extreme gradient & specificity of 97.9\% & & & \\  & Boosting Machine & (XGBoost) & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{4}{*}{[72]} & \multirow{4}{*}{Clinical Blood Test} & Six Classification & & & & \\  & Model & BayesNet, & & & \\  & logistic-regression, & & & & \\  & lazy-classifier, & & & & \\  & & meta-classifier, & & & \\  & classification via & & & & \\  & regression, & & & & \\  & decision-tree (J48) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cont. study [72] and the highest A.U.C. of 0.97 for another study [66]. Comparatively, the clinical blood test-based model obtained the highest A.U.C. of 0.97 [71] than RT-PCR-based models. In the C.T. scan method, a final chest image is captured by combining the images taken from various angles, while a C.T. scan requires a short time, but it is quite expensive. It can be observed that the C.T. image-based model obtained a minimum A.U.C. of 0.86 in the previous study [60] and maximum accuracy of 99.96% as per the investigation [50]. X-rays are used to create images of the chest. An X-ray is economically affordable in most areas. Therefore, most of the medical experts utilized X-ray images instead of C.T. images. An X-ray image-based model was observed with the lowest accuracy of 86.7% in a previous study [40] and the highest accuracy of 99.7% for another study [55]. Currently, ML/DL has played a significant role in boosting many CAD systems' diagnostic efficiency for various medical applications such as diagnosing and detecting different pulmonary diseases. Recent studies have shown that DL techniques have proven to be very efficient for CAD systems in radiography [48]. Hence, C.T. scan and X-ray both are widely used imaging methodologies for detecting various diseases and COVID-19. While X-rays need less data memory, processing time requires a low radiation dose than a C.T. scan [74]. Thus, CAD systems present vital solutions to boost and support radiologist workflow in predicting COVID-19 using low-dose X-ray images and overcoming limitations.