# Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms
## 5 Experimental Results
Table 6 displays the performance of each model with all evaluation metrics and TP. Namely, the proposed model achieved a high validation accuracy of 99.3% compared to other traditional pre-trained models. Figure 9 presents the confusion matrices for different pre-trained models, and Figure 10 demonstrates their respective ROC curves. It is worth mentioning that our approach achieved high accuracy \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Optimizer** & **ACC** & **PER** & **REC** & **F1-Score** & **Losses** & **tp (sec)** \\ \hline AdaGrad & 72.4\% & 84\% & 55\% & 53\% & 0.67 & 842 \\ AdaDelta & 83.9\% & 86.3\% & 79.6\% & 80.3\% & 0.44 & 841 \\ SGD & 91.8\% & 91\% & 92\% & 91\% & 0.25 & 890 \\ Adam & 95\% & 95\% & 98\% & 96\% & 0.08 & 845 \\ AdaMax & 97.4\% & 96.3\% & 98.3\% & 76\% & 0.07 & 847 \\ RMSprop & 99.3\% & 99\% & 99\% & 99.3\% & 0.02 & 862 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall evaluation of the proposed model with different optimizers and the LeakyReLU activation function (\(\alpha\) = 0.1). \begin{table}
\begin{tabular}{l c c c} \hline \hline  & & \multicolumn{3}{c}{**Class**} \\ \hline
**Optimizer** & **Metrics** & **COVID-19** & **Normal** & **Pneumonia** \\ \hline \multirow{4}{*}{AdaGrad} & Precision & 1.00 & 0.83 & 0.70 \\ \cline{2-5}  & REC & 0.02 & 0.68 & 0.95 \\ \cline{2-5}  & F1-Score & 0.04 & 0.74 & 0.81 \\ \cline{2-5}  & Accuracy & 100\% & 82.5\% & 70.2\% \\ \hline \multirow{4}{*}{AdaDelta} & PER & 1.00 & 0.73 & 0.86 \\ \cline{2-5}  & REC & 0.57 & 0.93 & 0.89 \\ \cline{2-5}  & F1-Score & 0.73 & 0.81 & 0.87 \\ \cline{2-5}  & ACC & 100\% & 72.5\% & 86\% \\ \hline \multirow{4}{*}{SGD} & PER & 0.99 & 0.79 & 0.95 \\ \cline{2-5}  & REC & 0.90 & 0.95 & 0.91 \\ \cline{2-5}  & F1-Score & 0.94 & 0.86 & 0.93 \\ \cline{2-5}  & ACC & 99.1\% & 79\% & 95.3\% \\ \hline \multirow{4}{*}{Adam} & PER & 1.00 & 0.81 & 1.00 \\ \cline{2-5}  & REC & 0.99 & 1.00 & 0.92 \\ \cline{2-5}  & F1-Score & 1.00 & 0.90 & 0.96 \\ \cline{2-5}  & ACC & 100\% & 81.4\% & 100\% \\ \hline \multirow{4}{*}{AdaMax} & PER & 1.00 & 0.89 & 1.00 \\ \cline{2-5}  & REC & 1.00 & 0.99 & 0.96 \\ \cline{2-5}  & F1-Score & 1.00 & 0.94 & 0.98 \\ \cline{2-5}  & ACC & 100\% & 89.4\% & 99.7\% \\ \hline \multirow{4}{*}{RMSprop} & PER & 1.00 & 0.97 & 1.00 \\ \cline{2-5}  & REC & 0.99 & 0.99 & 0.99 \\ \cline{1-1} \cline{2-5}  & F1-Score & 1.00 & 0.98 & 1.00 \\ \cline{1-1} \cline{2-5}  & ACC & 100\% & 97.4\% & 99.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of each class and overall accuracy using different optimizers with the LeakyReLU activation function set at \(\alpha\) = 0.1.  
with the advantage of being shallow (unlike the deep pre-trained models) with a low training burden (10 epochs). Our approach also investigated using different activation functions and optimizers. In addition, our approach results are promising and are on a comparable bar with other DL studies for COVID-19 detection. The results of other studies on DL with the details of their respective datasets are illustrated in Table 7.  
Figure 4: The loss curves of the proposed model. Figure 5: Confusion matrices of the proposed model with different optimizers using the LeakyReLU activation function set at (\(\alpha\) = 0.1). (**a**) AdaGrad, (**b**) AdaDelta, (**c**) SGD, (**d**) Adam, (**e**) AdaMax, and (**f**) RMSprop. Figure 6: ROC curves of the proposed model with different optimizers using the LeakyReLU activation function set at (\(\alpha\) = 0.1). (**a**) AdaGrad, (**b**) AdaDelta, (**c**) SGD, (**d**) Adam, (**e**) AdaMax, and (**f**) RMSprop.