## 3 Results
### Round 4 and 5 Training and Testing
Comparing the four models, much lower precision and accuracy were observed for VGG-19 than for other models (i.e., 67% vs. 80 \(\pm\) 1% and 74% vs. 85 \(\pm\) 1.5%, respectively), as shown in Figure 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Round 5 (\%)** & **Level 1** & **Level 2** & **Level 3** & **Level 4** \\ \hline \multirow{4}{*}{AlexNet} & Accuracy & 98.85 & 98.28 & 84.30 & 94.25 \\  & AUC & 99.96 & 99.90 & 97.66 & 99.91 \\  & Specificity & 98.01 & 97.64 & 84.89 & 98.99 \\  & Sensitivity & 99.26 & 98.64 & 83.50 & 84.19 \\  & Precision & 99.02 & 98.64 & 80.37 & 97.52 \\ \hline \multirow{4}{*}{ResNet-50} & Accuracy & 98.45 & 98.45 & 83.06 & 95.89 \\  & AUC & 99.91 & 99.96 & 95.97 & 99.92 \\  & Specificity & 97.61 & 97.17 & 83.45 & 94.56 \\  & Sensitivity & 98.87 & 99.19 & 82.52 & 98.72 \\  & Precision & 98.82 & 98.39 & 78.70 & 89.53 \\ \hline \multirow{4}{*}{MobileNet} & Accuracy & 99.04 & 98.97 & 86.78 & 96.16 \\  & AUC & 99.88 & 100.00 & 97.09 & 99.98 \\  & Specificity & 97.81 & 97.64 & 84.17 & 98.99 \\  & Sensitivity & 99.66 & 99.73 & 90.29 & 90.17 \\  & Precision & 98.93 & 98.66 & 80.87 & 97.69 \\ \hline \multirow{4}{*}{VGG-19} & Accuracy & 95.02 & 93.46 & 73.55 & 91.92 \\  & AUC & 99.19 & 99.31 & 91.42 & 99.93 \\ \cline{1-1}  & Specificity & 88.63 & 84.43 & 71.94 & 96.57 \\ \cline{1-1}  & Sensitivity & 98.18 & 98.64 & 75.73 & 82.05 \\ \cline{1-1}  & Precision & 94.59 & 91.69 & 66.67 & 91.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Round 5 performance comparison among models (AlexNet, ResNet-50, MobileNet, and VGG-19) that were trained on Base and tested on samples with varying similarities (Level 1–4) for three-class classifications (normal, COVID-19, pneumonia). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Round 4 (\%)** & **Level 1** & **Level 2** & **Level 3** & **Level 4** \\ \hline \multirow{4}{*}{AlexNet} & Accuracy & 98.91 & 98.45 & 85.95 & 90.96 \\  & AUC & 99.95 & 99.98 & 97.73 & 99.89 \\  & Specificity & 98.01 & 97.17 & 86.33 & 98.59 \\  & Sensitivity & 99.36 & 99.19 & 85.44 & 74.79 \\  & Precision & 99.02 & 98.39 & 82.24 & 96.15 \\ \hline \multirow{4}{*}{ResNet-50} & Accuracy & 98.85 & 98.11 & 86.36 & 93.01 \\  & AUC & 99.93 & 99.87 & 96.00 & 99.98 \\  & Specificity & 97.51 & 96.23 & 84.17 & 90.73 \\  & Sensitivity & 99.51 & 99.19 & 89.32 & 97.86 \\  & Precision & 98.78 & 97.86 & 80.70 & 83.27 \\ \hline \multirow{4}{*}{MobileNet} & Accuracy & 99.08 & 98.28 & 85.54 & 93.15 \\  & AUC & 99.91 & 99.97 & 97.33 & 99.87 \\ \cline{1-1}  & Specificity & 98.21 & 97.17 & 85.61 & 98.79 \\ \cline{1-1}  & Sensitivity & 99.51 & 98.92 & 85.44 & 81.20 \\ \cline{1-1}  & Precision & 99.12 & 98.38 & 81.48 & 96.94 \\ \hline \multirow{4}{*}{VGG-19} & Accuracy & 94.86 & 94.32 & 71.49 & 90.55 \\ \cline{1-1}  & AUC & 98.94 & 99.01 & 91.01 & 99.70 \\ \cline{1-1}  & Specificity & 88.53 & 88.68 & 67.63 & 95.36 \\ \cline{1-1}  & Sensitivity & 97.98 & 97.56 & 76.70 & 80.34 \\ \cline{1-1}  & Precision & 94.54 & 93.75 & 63.71 & 89.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Round 4 performance comparison among models (AlexNet, ResNet-50, MobileNet, and VGG-19) that were trained on Base and tested on samples with varying similarities (Level 1–4) for three-class classifications (normal, COVID-19, pneumonia). This observation persisted in Round 4 (64% vs. 81 \(\pm\) 0.6% and 71% vs. 86 \(\pm\) 0.3%), Round 3 (66% vs. 81 \(\pm\) 1.2% and 74% vs. 86 \(\pm\) 1.4%), and Round 2 (55% vs. 69 \(\pm\) 3.6% and 62% vs. 74 \(\pm\) 2.9%), even though VGG-19 had an equivalent Level-3 precision and accuracy in Round 1 (48% vs. 48 \(\pm\) 4.1% and 56% vs. 56 \(\pm\) 2.4%, Figure 4). Thus, based on the datasets in this study, VGG-19 exhibited a diminished response to re-training with extended data, compared to the other three models.