## 4 Experimental Results
A comparison of tables revealed that the proposed method had the best performance. It is evident that applying weights based on the color tendency of the training images was effective. To validate the proposed method, the network was trained with an equal number of training images of conventional methods. For this, we randomly selected 2880 training images from the images generated by the proposed method. The model is trained three times using selected training images. In case of noiseless images, the average of the classification accuracy of the model trained by the 2880 training images from the images generated by the proposed method was 87.3%. The classification performance on the noisy images was 67.0%. This additional experiment shows that the proposed method could augment the training images effectively. Figure 8 represents the comparison of the classification accuracy, with respect to average value and best performance. This figure shows that the more the proposed algorithms are combined, the higher the classification accuracy. Furthermore, the proposed method outperformed the conventional method. Therefore, the proposed method could improve the classification accuracy of both noisy and noiseless test images. We compared the confusion matrices of the experimental results for the training images in Figures 9 and 10. The left and right columns represent the classification results for the original and noisy test images, respectively. The experimental results for the original test images showed that the classification accuracy improved significantly after application \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Number of** \\ **Experiments** \\ \end{tabular} & \begin{tabular}{c} **Method** \\ **Training Images** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Noiseless** \\ \end{tabular} & \
\begin{tabular}{c} **Classification Accuracy** \\ **Noisely** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ \end{tabular} \\ \hline
1 & Training using & & & 92.0\% & 76.0\% & 84.0\% \\
2 & adaptive inverse & 69,120 & 91.0\% & 73.0\% & 82.0\% \\
3 & PSNR + ROT images & & 93.0\% & 72.0\% & 82.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification accuracy of the network trained using images obtained by the adaptive inverse PSNR and RGB color space and rotation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Number of** \\ **Experiments** \\ \end{tabular} & \begin{tabular}{c} **Method** \\ **Method** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Training Images** \\ \end{tabular} & \begin{tabular}{c} **Classification Accuracy** \\ **Noisely** \\ \end{tabular} & \
\begin{tabular}{c} **Classification Accuracy** \\ **Total** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ \end{tabular} \\ \hline
1 & Training using & & 68.0\% & 30.0\% & 49.0\% & \\
2 & R PSNR images & 1920 & 57.0\% & 34.0\% & 45.5\% & 47.5\% \\
3 & & & 56.0\% & 40.0\% & 48.0\% & \\
4 & & & 55.0\% & 26.0\% & 40.5\% & \\
5 & Training using & 1920 & 60.0\% & 42.0\% & 51.0\% & 46.8\% \\
6 & & & 61.0\% & 37.0\% & 49.0\% & \\
7 & & & 66.0\% & 46.0\% & 56.0\% & \\
8 & & & 58.0\% & 38.0\% & 48.0\% & 51.2\% \\
9 & & & 67.0\% & 32.0\% & 49.5\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification accuracy of the network trained using images obtained by applying the inverse PSNR to three channels individually. Table 7 presents the precision, recall, specificity, and F1-score of the conventional method and the proposed method. Precision, recall, specificity, and F1-score were calculated using (6). \[ \begin{split}\text{Precision}&=\frac{\text{True Positive}}{\text{True Positive}+\text{False Positive}}\\ \text{Recall}&=\frac{\text{True Positive}}{\text{True Positive }+\text{False Negative}}\\ \text{Specificity}&=\frac{\text{True Negative}}{\text{True Negative }+\text{False Positive}}\\ \text{F1-score}&=2\frac{\text{Recall}\times\text{Precision}}{ \text{Recall}+\text{Precision}}\\ \end{split}