## 3 Results
### Quantitative Results
#### 3.2.1 Image Classification  
We train two neural networks with the NEU-DET dataset to demonstrate that the proposed data augmentation method affects image classification tasks. MobileNetV2 Results  
With augmented validation data and original validation data, the average and standard deviation of the classification accuracy and the F1 score are obtained and shown in Table 8.  
Due to balanced datasets, the accuracy and F1 score tend to be the same. Moreover, the networks trained by the proposed three-channel augmented color dataset has higher accuracy and lower standard deviation. In the validation results with the original dataset, the average accuracy of the networks trained with the one-channel augmented dataset is higher than that of networks trained with the three-channel augmented color dataset. However, the networks trained with the three-channel augmented color dataset have a lower standard deviation. Figure 12 shows the boxplots of validation accuracy on each dataset. Figure 12a is the validation results with the augmented dataset, and Figure 12b is the validation results with the original dataset. The network trained by the three-channel augmented color dataset shows good accuracy than the network trained by the original dataset in both validation results. The networks trained with the one-channel augmented dataset do not have higher accuracy in Figure 12a. However, although it has an outlier that cannot assume consistent performance, it has higher accuracy in Figure 12b. The networks trained with the three-channel augmented grayscale dataset have an outlier in Figure 12a. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Original**} & \multicolumn{2}{c}{**One-Channel**} & \multicolumn{2}{c}{**Three-Channel Grayscale**} & \multicolumn{2}{c}{**Three-Channel Color**} \\ \cline{3-10}  & & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline Augmented & accuracy & 0.990 & 0.008 & 0.991 & 0.004 & 0.998 & 0.002 & 0.999 & 0.001 \\ \cline{2-10} dataset & F1 score & 0.990 & 0.007 & 0.991 & 0.004 & 0.998 & 0.002 & 0.999 & 0.001 \\ \hline Original & accuracy & 0.990 & 0.008 & 0.998 & 0.003 & 0.994 & 0.002 & 0.997 & 0.002 \\ \cline{2-10} dataset & F1 score & 0.990 & 0.007 & 0.998 & 0.003 & 0.994 & 0.005 & 0.997 & 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average and standard deviation of MobileNetV2 validation result. By training with the three-channel augmented color dataset, we can assume that the performance of networks will not fall below expected performance in both cases. Resnet18 Results  
With augmented validation data and original validation data, the average and standard deviation of the classification accuracy and the F1 score are obtained and shown in Table 9.  
The validation accuracy of Resnet18 is lower than that of MobileNetV2. ResNet18 has more parameters to train than MobilenetV2. Since only two epochs have been trained, it can be expected that Resnet18 is not optimized parameters to classify the NEU-DET dataset. However, the tendency of validation results via trained datasets can be confirmed. Similar to the validation results of MobileNetV2, accuracy and F1 score tend to be the same. Moreover, the average validation accuracy of the networks trained with the three-channel augmented color dataset is higher than other results. Figure 13 shows the boxplots of validation accuracy on each dataset. Figure 13a is the validation results with the augmented dataset, and Figure 13b is the validation results with the original dataset. The network trained by the three-channel augmented color dataset shows good accuracy than the network trained by the original dataset in both validation results. Figure 12: Boxplots of validation accuracy with MobileNetV2: (**a**) results validate with the augmented dataset; (**b**) results validate with the original dataset.