## 4 Experimental Results
### Using Parameter Optimization in Experiment 1  
In parameter optimization, 18 parameter combinations were conducted through the orthogonal array, and each experiment was repeated five times. The training parameters of AlexNet are presented in Table 11. Table 12 lists the five observations and the S/N ratio. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Size of Data Set** & **Original** & **Double** & **Triple** & **Quadruple** \\ \hline \hline \multicolumn{1}{c}{Accuracy} & 96.87\% & 99.18\% & 99.60\% & 99.60\% \\ \multicolumn{1}{c}{Sensitivity} & 94.30\% & 98.80\% & 99.30\% & 99.80\% \\ \multicolumn{1}{c}{Specificity} & 99.30\% & 99.60\% & 99.90\% & 99.40\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Accuracy, specificity, and sensitivity of generated image verification. Figure 14: Number of images and the accuracy of verification process. According to the S/N ratio in Table 12, the optimal level based on each factor was analyzed and the significant factors were ranked. The results from 18 experiments were displayed in Table 13 and the best factors were the results mapping to Table 4. The best parameter combination for SPIE-AAPM data set classification is C1-KS3, C1-S1, C1-P1, C5-KS2, C5-S3, C5-P1. The highest accuracy using the best parameter combination achieved is 99.99% in this study. The accuracy is considerably higher than other networks, besides, the training time is less than those networks. The results are shown in Table 14. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Training Epochs**} & \multicolumn{2}{c}{**Mini Batch Size**} & \multicolumn{2}{c}{**Optimizer Type**} & \multicolumn{2}{c}{**Base Learning Rate**} \\ \hline
2 & 15 & SGDM & 0.0003 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Training parameters of AlexNet. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**Sensitivity (\%)**} & \multicolumn{2}{c}{**Specificity (\%)**} & \multicolumn{2}{c}{**Training Time**} \\ \hline AlexNet & 97.48 & 95.1 & 99.8 & 3 min 50 s \\ GoogLeNet & 99.6 & 99.2 & 99.9 & 16 min 14 s \\ VGG16 & 99.57 & 99.2 & 99.9 & 19 min 59 s \\ VGG19 & 97.32 & 99.7 & 95 & 21 min 6 s \\ ResNet18 & 99.75 & 99.3 & 99.8 & 9 min 17 s \\
**AlexNet with** & **99.9** & **99.9** & **100** & **3 min 25 s** \\
**Taguchi method** & \multicolumn{2}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparison of the best parameter combination with other methods. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Training Epochs**} & \multicolumn{2}{c}{**Mini Batch Size**} & \multicolumn{2}{c}{**Optimizer Type**} & \multicolumn{2}{c}{**Base Learning Rate**} \\ \hline
2 & 15 & SGDM & 0.0003 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Five times observations and the S/N ratio. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Training Epochs**} & \multicolumn{2}{c}{**Mini Batch Size**} & \multicolumn{2}{c}{**Optimizer Type**} & \multicolumn{2}{c}{**Base Learning Rate**} \\ \hline
2 & 15 & SGDM & 0.0003 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Training parameters of AlexNet. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Training Epochs**} & \multicolumn{2}{c}{**Mini Batch Size**} & \multicolumn{2}{c}{**Optimizer Type**} & \multicolumn{2}{c}{**Base Learning Rate**} \\ \hline
2 & 15 & SGDM & 0.0003 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Five times observations and the S/N ratio. Overall, from experiment one and two, considering the size of the data, the better augment size might be double or triple. The accuracy shows significant improvement in those sizes of data set. In addition, AlexNet with optimal parameter combination shows better accuracy and lower standard deviation, which is more stable than the original AlexNet.