## 5 Results and Discussion  
In this section, we list the results obtained by using the proposed approach to classify chest X-rays into three classes (pneumonia, COVID-19, and normal) and two classes (normal and COVID-19). Table 5 shows the models' accuracy, sensitivity, and specificity performance. As can be observed from Table 6, the best performance for triple classification was obtained using the Db2 dataset, with 97.50% accuracy and 97.58% sensitivity. While the worst performance for triple classification was obtained using the Db1 dataset, with 95.14% accuracy and 93.60% sensitivity. As for binary classification, the best performance was obtained using the Db3 dataset, with 99.76% accuracy and 100% sensitivity. However, the worst accuracy in binary classification (97%) was obtained using the same dataset (Db1). That means the DBI\({}^{\prime}\)s performance did not work well with our model. Table 7 lists the precision, recall, and \(F_{1}\)-scores for datasets Db1, Db2, and Db3. As shown in Table 7, the best COVID-19 classification in the binary classification analysis was 100% in terms of \(F_{1}\)-score using DB3, and the best normal classification in the binary classification analysis was (also 100%) in terms of \(F_{1}\)-score using DB3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Model** & **Classes** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** \\ \hline \multirow{2}{*}{DB1} & \multirow{2}{*}{VGG16} & Binary & 97 & 90.85 & 98.3 \\ \cline{3-6}  & & Multi & 95.14 & 93.60 & 94.76 \\ \hline \multirow{2}{*}{DB2} & \multirow{2}{*}{VGG16} & Binary & 98.73 & 98.18 & 99.10 \\ \cline{2-6}  & & Multi & 97.50 & 97.58 & 98.48 \\ \hline \multirow{2}{*}{Db3} & \multirow{2}{*}{VGG16} & Binary & 99.76 & 100 & 99.68 \\ \cline{2-6}  & & Multi & 96.50 & 96.30 & 97.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of convolutional neural network (accuracy, sensitivity, and specificity) with all datasets and both models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Model** & **Classes** & **Precision** & **Recall** & \(F_{1}\)**-Score** \\ \hline \multirow{2}{*}{DB1} & \multirow{2}{*}{VGG16} & Normal & 98\% & 98\% & 98\% \\ \cline{3-6}  & & COVID & 94\% & 91\% & 92\% \\ \hline \multirow{2}{*}{DB2} & \multirow{2}{*}{VGG16} & Normal & 98\% & 99\% & 99\% \\ \cline{3-6}  & & COVID & 99\% & 98\% & 98\% \\ \hline \multirow{2}{*}{DB3} & \multirow{2}{*}{VGG16} & Normal & 100\% & 100\% & 100\% \\ \cline{3-6}  & & COVID & 99\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Precision, recall, and \(F_{1}\)-score values on all datasets using binary classification. The worst COVID-19 cases classification in the binary classification analysis was 92% in \(F_{1}\)-score using DB1. The worst normal cases classification in the binary classification analysis was 98% in terms of \(F_{1}\)-score using DB1. This is another reason that improving the Db1 dataset did not work well with the proposed model, unlike other datasets. Perhaps this is due to the significant disparity in the number of images between normal and COVID-19 images. For example, the Db1 dataset contains 10,192 normal images, while it contains 3616 COVID-19 images. Figures 7 and 8 show the confusion matrix, accuracy, and loss curves for Db2 and Db3. As shown in Figure 7, there are four subfigures taken from Db2. Figure 7a,d) refer to the loss function and accuracy for multi-classification and binary classification, respectively. In contrast, Figure 7b,c) show  
Figure 7: Confusion matrix and evaluation metrics for Db2. (**a**) Loss function and accuracy for multi-classification; (**b**) Confusion matrix for multi-classification; (**c**) Confusion matrix for binary classification; (**d**) The accuracy curve for binary classification. Figure 8: Confusion matrix and evaluation metrics for Db3.