## 6 Results and Discussion
### Kappa Statistic  
The kappa statistic denoted by \(k\) is a popular evaluation metric in statistics. It indicates the relative measure of reliability within different judges or evaluators. The subsequent equation reckons the value of \(k\) as:  
\[k=\frac{prob(O)-prob(C)}{1-prob(C)} \tag{6}\]  
Here, \(prob(O)\) is the probability of perceived agreements amongst the evaluators and \(prob(C)\) is the probability of agreements expected by coincidence. If \(k=1\), then the evaluators have supported each other's judgment. If \(k=0\), then the evaluators do not reach agreement. Using 40 epochs, it is observed that the VGG-19 classifier is responsible for providing accuracy of 87.45%, 85.91%, and 88.76% as presented in Tables 1-3, respectively, with respect to 70%-30% distribution of employed data and 88.35%, 88.12%, and 89.34% for 10-fold cross-validation according to the results mentioned in these tables. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{
\begin{tabular}{c} **CNN** \\ **Model** \\ \end{tabular} } & \multicolumn{2}{c}{**70\%–30\% Distribution**} & \multicolumn{2}{c}{**10-Fold Cross-Validation**} \\ \cline{2-7}  & **Accuracy (\%)** & **RMSE** & **Weighted Kappa** & **Accuracy (\%)** & **RMSE** & **Weighted Kappa** \\  & **(sd)** & **(sd)** & **(sd)** & **(sd)** & **(sd)** & **(sd)** \\ \hline \multirow{2}{*}{VGG-19} & 87.45 & 0.2285 & 0.8610 & 88.35 & 0.2195 & 0.8700 \\  & (\(\pm\)0.03) & (\(\pm\)0.03) & (\(\pm\)0.03) & (\(\pm\)0.03) & (\(\pm\)0.03) & (\(\pm\)0.03) \\ \hline \multirow{2}{*}{ResNet-50} & 94.38 & 0.1592 & 0.9303 & 97.61 & 0.1169 & 0.9726 \\  & (\(\pm\)0.04) & (\(\pm\)0.03) & (\(\pm\)0.04) & (\(\pm\)0.03) & (\(\pm\)0.03) & (\(\pm\)0.03) \\ \hline \multirow{2}{*}{Inception-V3} & 95.62 & 0.1468 & 0.9427 & 99.23 & 0.1107 & 0.9788 \\  & (\(\pm\)0.04) & (\(\pm\)0.03) & (\(\pm\)0.03) & (\(\pm\)0.03) & (\(\pm\)0.03) & (\(\pm\)0.04) \\ \hline \end{tabular} sd: standard deviation.  
\end{table} 
Table 1: Comparison of CNN models using dataset 1 for 40 epochs. On the other hand, the accuracy of the ResNet-50 model lies between 93.9% and 95.64% with respect to the 70%-30% distribution and between 96.8% and 97.61% for the 10-fold cross-validation concerning 40 epochs as shown in Tables 1-3. The accuracy of Inception-V3 for such three benchmark datasets regarding 40 epochs ranges from 95.62% to 96.48% for the 70%-30% distribution and from 99.2% to 99.36% utilizing the 10-fold cross-validation in accordance with the results presented in these tables. The performance of employed classifiers in this research work in accordance with accuracy for these three datasets regarding 40 epochs is revealed by Figure 7 and it also represents that Inception-V3 possesses supremacy over VGG-19 and Resnet-50 for accuracy taking such employed datasets into consideration. RMSE and kappa statistic are also regarded as powerful metrics to compare various classifiers. To achieve decent performance of a classifier, RMSE should be lowest comparing RMSE of other classifiers taken into account. Previous research articles for COVID-19 detection did not account for RMSE and kappa statistic to prove the superiority of their proposed work. However, this work emphasized RMSE as a comparative measuring metric to differentiate the performance of employed classification models. Tables 1-3 reveal the dominance of Inception-V3 as it provides the lowest RMSE comparing the RMSEs provided by VGG-19 and ResNet-50. The lowest RMSE possessed by Inception-V3 for these employed datasets indicates its superiority, as depicted in Figure 8. Tables 1-3 denote a variation between 0.8-1.0 showing \"almost perfect agreement\" for kappa statistic values and exhibiting the superiority of Inception-V3 over VGG-19 and Resnet-50 with respect to these performance-evaluating metrics used in the testing phase. Figure 9 also represents the better performance of Inception-V3 than other two utilized classifiers in this proposed work.