# COVID-19 CXR Classification: Applying Domain Extension Transfer Learning and Deep Learning
## 4 Results
### Evaluation Results  
Table 7 shows the results of calculating the performance of the model to which the proposed methodology was applied using the performance evaluation indicators. Using the methodology proposed, a classification accuracy level of 96.7%, a precision level of 96.2%, a sensitivity level of 96.1%, a specificity level of 97.2%, an F1-score of 96.1%, an MCC level of 0.93, and a balanced accuracy level of 96.6% were obtained. The proposed model improved accuracy by 9.9%, precision by 13.1%, sensitivity by 9.6%, specificity by 10.2%, the F1-score by 11.9%, MCC by 0.2, and balance accuracy by 9.9% in each part. Compared with the baseline model, the classification performance of the model improved when the proposed methodology was applied. It was confirmed that the performance was significantly improved in sensitivity and specificity, which are important performance measures in the biomedical field. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Predicted Class**} \\ \cline{3-5}  & & **COVID-19** & **Non-COVID-19** & **Total** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **Actual** \\ **Class** \\ \end{tabular} } & **COVID-19** & \(N_{11}\) & \(N_{12}\) & \(\sum\limits_{i=1}^{2}N_{1i}\) \\ \cline{2-5}  & **Non-COVID-19** & \(N_{21}\) & \(N_{22}\) & \(\sum\limits_{i=1}^{2}N_{2i}\) \\ \cline{2-5}  & **Total** & \(\sum\limits_{i=1}^{2}N_{1i}\) & \(\sum\limits_{i=1}^{2}N_{2i}\) & \(N\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix of binary classification. Figure 7 shows a confusion matrix for the actual and predicted values of the COVID-19 CXR binary classification. Figure 7a shows the classification result of the baseline model, and Figure 7b shows the classification result of the model to which the proposed methodology was applied. In the confusion matrix, the horizontal axis represents the predicted label value, and the vertical axis represents the actual label value. The values from the left-hand side to the right-hand side are from the good prediction of the actual label of the model. The baseline model showed a low accuracy for COVID-19 and non-COVID-19 images. However, the model to which the proposed methodology was applied showed a significant increase in the predicted label value of the actual label value. It can also be observed that the classification accuracy improved in all actual classes. Table 8 shows the results of the comparative experiment conducted by applying the MIMIC CXR and MIMIC CXR (PA) data to ResNet-50. The baseline model results are from the image classification by training and testing the ResNet-50, using Mendeley datasets. The model to which the proposed methodology was applied used the ResNet-50, which was pretrained using natural images and pretrained using the MIMIC CXR (PA) data based on domain extension transfer learning, after which fine-tuning was performed using the Mendeley dataset. Model 1 is the result of fitting the ResNet-50 model pretrained using natural images to the Mendeley dataset. Models 2 and 3 are the results of applying domain extension transfer learning to the Mendeley datasets through ResNet-50 models pretrained using MIMIC CXR and MIMIC CXR (PA) data, respectively. Finally, Model 4 is the result of the proposed methodology using the MIMIC CXR dataset. The experimental results demonstrate that the PA image datasets significantly improve the classification performance of CXR images and that the use of pretrained models is more effective in classifying images. Additionally, the proposed methodology for pretraining and fine-tuning the target domain rather than simply using a pretrained model is most effective in classifying COVID-19 CXR images.