## 3 Results and Discussions
### COVID-19 Classification Results  
We trained three convolution-based and two transformer-based models on the customized COVID-19 classification dataset. The output results were evaluated based on three metrics, which were precision, recall, and F1-score with respect to COVID-19, normal, pneumonia, macro-average, and micro-average. Each tables below contained numerical results of three metrics and will be described in detail. The two models which showed the best results were DenseNet121 and Hybrid EfficientNet-DOLG. Table 4 compares the performance of five deep learning models based on the precision metric score. The numerical results illustrate each category and on macro- and micro-average. For the COVID-19 precision score, the best model was found to be Swin Transformer with a score of 0.99, and for normal images, we found Hybrid EfficientNet-DOLG a top score of 0.93, and for pneumonia, the DensNet121 model produced the highest result (0.94). For macro-average and micro-average, the Hybrid EfficientNet-DOLG was found to lead both metrics with scores of 0.95 and 0.96, respectively. The hierarchical architecture of Swin Transformer computed image representation in different scales which works best on COVID-19. On the other hand, the concatenate mechanism of DensNet boosted the pneumonia detection of DenseNet. The hybrid architecture helped Hybrid EfficientNet-DOLG perform best results on normal, macro average, and micro average. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Precision**} \\ \cline{2-6}  & **COVID-19** & **Normal** & **Pneumonia** & **Macro-** & **Average** \\ \hline DenseNet121 & 0.98 & 0.91 & **0.94** & 0.94 & 0.95 \\ \hline ResNet50 & 0.98 & 0.83 & 0.93 & 0.92 & 0.94 \\ \hline InceptionNet & 0.98 & 0.83 & 0.90 & 0.90 & 0.92 \\ \hline Swin Transformer & **0.99** & 0.62 & 0.89 & 0.83 & 0.87 \\ \hline Hybrid & 0.98 & **0.93** & 0.93 & **0.95** & **0.96** \\ EfficientNet-DOLG & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparing results between the five deep learning models on the COVID-19 classification task with respect to precision metric score. As shown in Table 5, we could see the outstanding performance of Hybrid EfficientNet-DOLG as it produced the highest score on three metrics: pneumonia, macro-average, and micro-average, with scores of 0.95, 0.96, and 0.96, respectively. For the COVID-19 category, we found the DenseNet121 to have a recall score of 0.98 and the second highest score was found to be Hybrid EfficientNet Transformer which was 0.97, only smaller than a 0.01 gap. For normal, we found Swin Transformer to have a score of 0.97 and the second score was DenseNet 121 and Hybrid EfficientNet-DOLG, which was 0.94. The other models, including ResNet50 and InceptionNet, also computed comparable results. With respect to recall, the dense architecture of DenseNet worked best on COVID-19 images, and different from precision metric, the hierarchy architecture of Swin Transformer achieved the best results on normal images. The combination of EfficientNet and DOLG took the best place on pneumonia, macro average, and micro average. The third metric was the F1-score; in this metric, we could see the pattern that Hybrid EfficientNet produced the best results on all scores except the normal category, in which the top result belonged to DenseNet with a score of 0.95. For COVID-19, pneumonia, macro-average, and micro-average the Hybrid EfficientNet-DOLG produced the highest results with scores of 0.99, 0.94, 0.95, and 0.96, respectively. For COVID-19, pneumonia, macro-average, and micro-average, the second highest model was DensNet121, which produced scores of 0.98, 0.93, 0.94, and 0.95 sequentially. Different from precision and recall, the classification performance of DenseNet secured best on normal images, this might be because the dense connections performed well for normal cases regarding the F1-score, then the compound of EfficientNet and DOLG did efficiently on other categories.