## 3 Results and Discussions
### Severity Assessment Results  
With the COVID-19 severity assessment task, we also trained the customized COVID-19 severity dataset with five models: DenseNet121, ResNet50, InceptionNet, Swin Transformer, and Hybrid EffificientNet-DOLG. The dataset contains images of two categories, level1 and level2, in which level1 indicates the patient severity is normal and the patient can self-quarantine at home without requiring a further treatment response. Level2 indicates that the patients need to have further support and need to go to the hospital for a treatment response because the pneumonia extent of COVID-19 damage is large and severe. After training models for hours, we obtained the output results as shown in Tables 7-9. We also evaluated severity assessment results on three metrics: precision, recall, and F1-score with respect to level1, level2, macro-average, and micro-average, the same as the COVID-19 classification task. A detailed analysis of deep learning models' performance on COVID-19 severity assessment is presented for each metric under every table. From Table 7, we can see that with level1, DenseNet121 output had the top precision score result of 0.76, and level2 Hybrid EfficientNet-DOLG produced a precision score of 0.87. The Swin Transformer led the macro-average with a 0.81 precision score, and with respect to the micro-average, the Hybrid Efficient-DOLG also produced the highest  
Figure 6: Confusion matrix of (**a**) DenseNet and (**b**) Hybrid EfficientNet-DOLG on the COVID-19 classification task. Darker colors illustrate larger number of the correct predicted labels. Figure 7: Training history of (**a**) DenseNet and (**b**) Hybrid EfficientNet-DOLG on the COVID-19 classification task. score of 0.82. The dense connection of DenseNet helped to reduce gradients vanishing which improve the precision metric of DenseNet on level1 chest X-ray images. The Swin Transformer produced the best on macro average because the accuracy of level1 and level2 is very high, and Hybrid EfficientNet-DOLG surpassed other neural nets on micro average because its precision on level2 is the highest and on level1 almost equal DenseNet. When comparing the precision score, we could see that the Hybrid EfficientNet-DOLG outperformed on all three categories: level1, macro-average, and micro-average with the score of 0.75, 0.80, and 0.82. The DenseNet121 produced the highest score of level2 (0.89) on the recall metric. The dense connection mechanism of DenseNet is sensitive to level2 chest X-ray images that made up the highest score for DenseNet on the recall metric. The EfficientNet encoder of the hybrid network produces overall superb performance on both level1, macro-average, and micro-average. Swin Transformer and other two convolution-based neural nets, ResNet50 and InceptionNet, also achieved comparable results with DenseNet and Hybrid EfficientNet-DOLG. The last metric we analyzed was F1-score. In this metric, Hybrid EfficientNet-DOLG outperformed all four categories: level1, level2, macro-average, and micro-average, with scores of 0.74, 0.86, 0.80, and 0.82. The top results on the F1-score of Hybrid EfficientNet-DOLG are based on the robustness of EfficientNet as an encoder of the structure and combine with the global and local descriptor of DOLG. DenseNet also produced high output results with scores of 0.71 on level1, 0.86 on level2, 0.79 on macro-average, and 0.81 \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Precision**} \\ \cline{2-5}  & **Level1** & **Level2** & **Macon-Average** & **Micro-Average** \\ \hline DenseNet121 & **0.76** & 0.84 & 0.80 & 0.81 \\ \hline ResNet50 & 0.74 & 0.77 & 0.76 & 0.76 \\ \hline InceptionNet & 0.72 & 0.77 & 0.75 & 0.75 \\ \hline Swin Transformer & 0.75 & 0.86 & **0.81** & 0.80 \\ \hline Hybrid EfficientNet-DOLG & 0.74 & **0.87** & 0.80 & **0.82** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparing results between the five deep learning models on COVID-19 severity assessment task with respect to precision metric.