# Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms
## 5 Experimental Results
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Optimizer** & **ACC** & **PER** & **REC** & **F1-Score** & **Losses** & **tp (sec)** \\ \hline AdaGrad & 73.5\% & 83.6\% & 57\% & 58\% & 0.66 & 859 \\ AdaDelta & 86\% & 87\% & 84\% & 84.3\% & 0.44 & 845 \\ SGD & 91.8\% & 90.6\% & 92.3\% & 91.3\% & 0.93 & 832 \\ RMSprop & 93.5\% & 92\% & 95.6\% & 93\% & 0.7 & 856 \\ Adam & 94\% & 92\% & 96\% & 94\% & 0.34 & 845 \\ AdaMax & 98.5\% & 98\% & 99\% & 98.6\% & 0.04 & 856 \\ \hline \end{tabular}
\end{table} 
Table 4: Overall evaluation of the proposed model with different optimizers and ELU activation function (\(\alpha\) = 0.2)  
Figure 7: Confusion matrices of the proposed model using the ELU activation function (\(\alpha\) = 0.2) with different optimizers. (**a**) AdaGrad, (**b**) AdaDelta, (**c**) SGD, (**d**) RMSprop, (**e**) Adam, and (**f**) AdaMax. \begin{table}
\begin{tabular}{l c c c c} \hline  & & \multicolumn{3}{c}{**Class**} \\ \hline
**Optimizer** & **Metrics** & **COVID-19** & **Normal** & **Pneumonia** \\ \hline \multirow{4}{*}{AdaGrad} & PER & 1.00 & 0.80 & 0.71 \\ \cline{2-5}  & REC & 0.11 & 0.67 & 0.95 \\ \cline{2-5}  & F1-Score & 0.20 & 0.73 & 0.81 \\ \cline{2-5}  & ACC & 100\% & 79.8\% & 71.4\% \\ \hline \multirow{4}{*}{AdaDelta} & PER & 1.00 & 0.72 & 0.89 \\ \cline{2-5}  & REC & 0.71 & 0.92 & 0.89 \\ \cline{2-5}  & F1-Score & 0.83 & 0.81 & 0.89 \\ \cline{2-5}  & ACC & 100\% & 72.3\% & 89\% \\ \hline \multirow{4}{*}{SGD} & PER & 0.98 & 0.78 & 0.96 \\ \cline{2-5}  & REC & 0.92 & 0.94 & 0.91 \\ \cline{2-5}  & F1-Score & 0.95 & 0.85 & 0.94 \\ \cline{2-5}  & ACC & 98.4\% & 77.8\% & 96\% \\ \hline \multirow{4}{*}{RMSprop} & PER & 1.00 & 0.76 & 1.00 \\ \cline{2-5}  & REC & 0.96 & 1.00 & 0.91 \\ \cline{2-5}  & F1-Score & 0.98 & 0.87 & 0.95 \\ \cline{2-5}  & ACC & 100\% & 76.2\% & 100\% \\ \hline \multirow{4}{*}{Adam} & PER & 1.00 & 0.78 & 1.00 \\ \cline{2-5}  & REC & 0.96 & 1.00 & 0.92 \\ \cline{2-5}  & F1-Score & 0.98 & 0.88 & 0.96 \\ \cline{2-5}  & ACC & 100\% & 78.1\% & 100\% \\ \hline \multirow{4}{*}{AdaMax} & PER & 1.00 & 0.94 & 1.00 \\ \cline{2-5}  & REC & 1.00 & 0.99 & 0.98 \\ \cline{2-5}  & F1-Score & 1.00 & 0.97 & 0.99 \\ \cline{2-5}  & ACC & 100\% & 93.8\% & 99.7\% \\ \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis of each class and overall accuracy using different optimizers with the ELU activation function set at \(\alpha=0.2\). Figure 8: ROC curves of the proposed model using the ELU activation function (\(\alpha=0.2\)) with different optimizers (a) AdaGrad, (b) AdaDelta, (c) SGD, (d) RMSprop, (e) Adam, and (f) AdaMax. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **ACC** & **PER** & **REC** & **F1-Score** & **Losses** & **tp (sec)** \\ \hline VGG16 & 60.8\% & 61\% & 100\% & 76\% & 0.94 & 690 \\ Xception & 61\% & 61\% & 100\% & 76\% & 0.92 & 853 \\ DenseNet121 & 72\% & 77\% & 79\% & 74\% & 0.58 & 693 \\ InceptionV3 & 72.8\% & 80\% & 85\% & 81\% & 0.84 & 658 \\ ResNet50 & 87\% & 84\% & 87\% & 85\% & 0.33 & 693 \\ MobilNet & 95.4\% & 77\% & 79\% & 74\% & 0.14 & 633 \\ Proposed model & 99.3\% & 99\% & 99\% & 99.3\% & 0.02 & 862 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative accuracy of our model with other CNN models. Learning rate \(\eta=0.1\), ReLU activation function, and Adam optimizer for all pre-trained models.