## 3 Results and Discussions
### Severity Assessment Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Recall**} \\ \cline{2-5}  & **Level1** & **Level2** & **Macon-Average** & **Micro-Average** \\ \hline DenseNet121 & 0.67 & **0.89** & 0.78 & 0.81 \\ \hline ResNet50 & 0.65 & 0.85 & 0.70 & 0.77 \\ \hline InceptionNet & 0.70 & 0.80 & 0.70 & 0.76 \\ \hline Swin Transformer & 0.72 & 0.80 & 0.73 & 0.75 \\ \hline Hybrid EfficientNet-DOLG & **0.75** & 0.86 & **0.80** & **0.82** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparing results between the five deep learning models on the COVID-19 severity assessment task with respect to recall metric. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**F1-Score**} \\ \cline{2-5}  & **Level1** & **Level2** & **Macon-Average** & **Micro-Average** \\ \hline DenseNet121 & 0.71 & 0.86 & 0.79 & 0.81 \\ \hline ResNet50 & 0.60 & 0.84 & 0.72 & 0.75 \\ \hline InceptionNet & 0.65 & 0.84 & 0.75 & 0.77 \\ \hline Swin Transformer & 0.62 & 0.84 & 0.63 & 0.69 \\ \hline Hybrid EfficientNet-DOLG & **0.74** & **0.86** & **0.80** & **0.82** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparing results between the five deep learning models on the COVID-19 severity assessment task with respect to F1-score metric. on micro-average. Output of DenseNet was only smaller than Hybrid EfficientNet-DOLG with 0.01 score gaps on level1, macro-average, and micro-average and produce the equal result on level2. Other neural nets also achieved comparable results on F1-score with DenseNet and Hybrid EfficientNet-DOLG. Overall, we can conclude that Hybrid EfficientNet-DOLG and DenseNet are two models which produce the highest inference results on the COVID-19 severity assessment task; it is the same pattern as the performance on the COVID-19 classification task. Figures 8 and 9 show us the confusion matrix and training history of DenseNet and Hybrid EfficientNet-DOLG after inferencing and training on the COVID-19 severity assessment dataset. There are many deep learning architecture and deep transfer learning techniques that could apply to X-ray imagery as a potential method for classifying COVID-19 detection and severity assessment. Some of the architecture techniques include Wide Residual Networks [61] (WRNs) and Visual Geometry Group [62] (VGG). Wide Residual Networks are variants of ResNet, which both increase the width and decrease the depth of residual networks, and also create lightweight models with high performance. With only 16 layers, the network can outperform another convolutional neural networks with over 1000 layers on CIFAR and ImageNet datasets. The second network is VGG, which won the ImageNet Challenge 2014 and was first and second in terms of localization and classification, respectively. We have VGG16 and VGG19, which represent 16 layers and 19 layers of neural networks. In general, the architecture of VGG includes input layers, convolutional layers, hidden layers, and fully connected layers, and depending on the architecture we use, that the number of layers might be different. There is research that studies the application of deep transfer learning, including the work of [PERSON] et al. [63] in which the author efficiently implemented deep transfer learning techniques for land use and land cover classification based on WSNs and VGG pre-trained models. Another study by [PERSON] et al. [64] tried to apply deep transfer learning automatically to detect COVID-19 based on chest X-ray images. Figure 8: Confusion matrix of (**a**) DenseNet and (**b**) Hybrid EfficientNet-DOLG on the COVID-19 severity assessment task. Darker colors illustrate larger number of the correct predicted labels. This study has some limitations. First, we collected data from many open-source datasets; to some extent this might affect the model accuracy. Because X-ray images obtained from different machines have various image qualities, image color channels as well as resolutions, these factors have significantly impact on the model training pipeline.