## 3 Results
### Round 4 and 5 Training and Testing  
For the Level 4 testing dataset, model classification accuracies were persistently observed to remain around 80% in Rounds 1-3. This was reasonable considering that the Rounds 1-3 models were trained predominately on Dataset 1, which might miss certain features inherent in Level 4 images (Dataset 3). This underscores the fact that simply adding irrelevant images to the training set does not enhance model performance, reinforcing the pivotal importance of data quality and relevance in model training. In Rounds 4 and 5, 25% and 50% of the Level 4 images (730) were added to the Round-3 training dataset, respectively. The performance metrics are listed in Tables 5 and 6. From Table 5, it was noted that the classification metrics at Levels 1-3 changed insignificantly (e.g., 0.1-2%) between Round 4 and Round 3 for all models considered. The metric variations were also mixed in trend. Sensitivity was the only exception, experiencing a decrease across all models. This suggests that introducing new, non-relevant features or images can undermine the model's capacity to recognize pertinent features. At Level 4, adding 25% Level-4 images to the Round-3 training dataset resulted in a substantial increase in all performance metrics. Compared to Round 3, the sensitivity increased by 27% (from 57% to 84%), accuracy by 11% (from 81% to 92%), precision by 9% (from 82% to 91%), specificity by 4% (from 92% to 96%), and AUC by 0.4% (from 99.5% to 99.9%), as shown in both Table 5 and Figure 3. Note that an average classification accuracy of 92% is deemed satisfactory in many clinical applications, underscoring the benefits of multi-round training with extended data, even when exposed to a limited set of new images (in this case, 25% of 730 or 183 images). Table 6 shows the Round-5 training/testing results after adding another 25% of Level-4 images to the Round-4 training dataset. The classification accuracy tested on the Level-4 images reached 95%, up from 92% in Round 4 (Figure 3). The sensitivity showed the largest magnitude of increase, i.e., 5% (from 84% to 89%). On the other hand, the specificity increased from 96% to 97% (1%) and the precision from 91% to 94% (3%), as shown in Figure 3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Round 3 (\%)** & **Level 1** & **Level 2** & **Level 3** & **Level 4** \\ \hline \multirow{4}{*}{AlexNet} & Accuracy & 99.11 & 98.80 & 85.54 & 82.05 \\  & AUC & 99.94 & 99.98 & 97.92 & 99.71 \\  & Specificity & 98.01 & 98.11 & 84.89 & 97.38 \\  & Sensitivity & 99.66 & 99.19 & 86.41 & 49.57 \\  & Precision & 99.02 & 98.92 & 80.91 & 89.92 \\ \hline \multirow{4}{*}{ResNet-50} & Accuracy & 98.45 & 97.76 & 88.02 & 77.12 \\  & AUC & 99.90 & 99.89 & 96.82 & 99.40 \\  & Specificity & 97.31 & 95.75 & 84.89 & 76.61 \\  & Sensitivity & 99.02 & 98.92 & 92.23 & 78.21 \\  & Precision & 98.68 & 97.59 & 81.90 & 61.20 \\ \hline \multirow{4}{*}{MobileNet} & Accuracy & 98.91 & 98.80 & 84.71 & 82.47 \\  & AUC & 99.89 & 100.00 & 96.91 & 99.78 \\ \cline{1-1}  & Specificity & 97.51 & 98.58 & 82.73 & 97.78 \\ \cline{1-1}  & Sensitivity & 99.61 & 98.92 & 87.38 & 50.00 \\ \cline{1-1}  & Precision & 98.78 & 99.18 & 78.95 & 91.41 \\ \hline \multirow{4}{*}{VGG-19} & Accuracy & 95.16 & 93.12 & 73.55 & 81.51 \\ \cline{1-1}  & AUC & 99.15 & 99.23 & 92.35 & 99.09 \\ \cline{1-1}  & Specificity & 89.43 & 84.91 & 69.78 & 95.56 \\ \cline{1-1}  & Sensitivity & 97.98 & 97.83 & 78.64 & 51.71 \\ \cline{1-1}  & Precision & 94.95 & 91.86 & 65.85 & 84.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Round 3 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). At Level 3, small variations with mixed trends were observed between Round 4 and 5 trained models. Overall, adding training images that were irrelevant to Level 3 led to a slight decrease in model performance (0.4% on average, Table 6).