## 4 Experimental Results
### COVID-19 Detection Results  
For detection, the deep models mentioned in Section 3.3.1 were trained and tested using the expanded datasets, which consist of 500 real images and 2790 generated images for each class. The percentage of generated images in the whole expanded dataset is 84.8%, as shown in Figure 11, which is more than five times the number of real images.  
Figure 10: Samples of MSE and SSIM values between three selected images and the other images.  
Figure 9: Confusion matrix of VGG16 based on TSTR.  
Figure 12 shows the number of real and generated images in the training and testing process. As shown in the figure, the training part was conducted on 77.81% of the dataset (100 real images and 630 generated images), and the testing part was conducted on 22.19% of the dataset (400 real images and 2160 generated images). To ensure that the expanded dataset was still balanced, the same number of images was generated for each class, as shown in Table 5.  
The kappa coefficient, which is one of the classifier's performance metrics, was used to measure the degree of agreement between the actual and predicted labels and to control for instances where an image may have been correctly classified by chance [49]. The kappa coefficient can be calculated using both the observed accuracy and the random accuracy as in Equations (11) and (12). According to [51], the kappa coefficient, which is between 0.81 and 1, indicates an almost perfect agreement.  
Figure 11: The percentage of real images and generated images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} \\ \cline{2-5}
**Class** & **No. of Real** & **No. of Generated** & **No. of Real** & **No. of Generated** \\  & **Images** & **Images** & **Images** & **Images** \\ \hline COVID-19 & 400 & 2160 & 100 & 630 \\ Normal & 400 & 2160 & 100 & 630 \\ Pneumonia & 400 & 2160 & 100 & 630 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The number of images for the training and testing process for each class.  
Figure 12: The number of real images and generated images in the training and testing process.  
Figure 13 shows that all five models obtained an almost perfect agreement between the actual labels and the predicted labels. Table 6 presents the performance of the five detection models based on accuracy, macro-precision, macro-recall, and macro-average F-score. As shown in the table, InceptionResNetV2 was the best-performing model for all metrics, whereas SqueezeNet was the worst-performing model with no apparent difference among all metrics. To obtain a better overview of the testing accuracy per class, we present the confusion matrices of the five deep transfer learning models in Figure 14.  
Choosing a suitable model is a tradeoff among different parameters such as speed, size, and accuracy. Figure 15 shows the five selected models and their corresponding parameters. The area of diamonds in the figure represents the model's size on the disk, meaning that VGG16 is the largest model with a size of 515 MB, whereas SqueezeNet is the smallest at only 4.5 MB. All models achieved high accuracy between 98.86% and 99.72%, but with different training times. The figure also shows that some models were faster or smaller compared to others but could still achieve high accuracy. For instance, SqueezeNet, which is a small network, required only three minutes and achieved comparable accuracy to larger networks such as InceptionResNetV2, which needed more time (143 min) to run.  
Figure 14: The confusion matrices of the deep transfer learning models.