## 4 Results
### Testing Processes
#### 4.2.2 Robustness with Different Data Augmentation Methods  
Further, we investigated the robustness of the method with different combinations of data augmentation methods. Random horizontal flip, random vertical flip, random rotation, and color jitter were elected. Nine kinds of combinations were applied to both the supervised learning (SL) and supervised contrastive learning (SCL). Details of the validation results are shown in Table 3 and Figure 6. As shown in Table 3 and Figure 6, besides the combination of methods, different rotation rates were also considered. The standard deviation of the SL was 1.53, while that of the SCL was 0.18. Therefore, the robustness of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Learning Rate** & **SL** & **SCL** \\ \hline
0.0001 & 69.42 & 86.45 \\
0.0005 & 80.33 & 86.48 \\
0.001 & 81.77 & 86.51 \\
0.003 & 82.59 & 86.51 \\
0.005 & 82.91 & 86.66 \\
0.01 & 82.63 & 86.74 \\
0.03 & 81.47 & 86.8 \\
0.05 & 83.26 & 86.74 \\
0.1 & 83.02 & 86.88 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of the different learning rates. Supervised contrastive learning (SCL) and supervised learning (SL).  
Figure 4: Error bar of accuracies with different learning rates.  
Figure 5: ROC curves with different learning rates for the supervised learning (SL) and supervised contrastive learning (SCL). The ROC curve max and ROC curve min stand for the ROC curves with the maximum AUC value and minimum AUC value, respectively.  
the SCL with different learning rates was better than the SL. Moreover, the mean accuracy of the SL was 80.68, while that of the SCL was 86.53. The SCL outperformed the SL again with different data augmentation methods.  
We also plotted the ROC curves and AUC values for the classification models with the SCL and SL for different data augmentation methods in Figure 7. The testing results indicated that the classification model with the SCL was better than the classification model with the SL. For a given threshold, the sensitivity and specificity of the model can be calculated. The sensitivity of the SL for the different data augmentation methods was \(81.82\%\pm 3.05\%\), while that of the SCL for the different data augmentation methods was \(86.53\%\pm 0.15\%\). The specificity of the SL for the different data augmentation methods is  
Figure 6: Error bar of accuracies with different data augmentation methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Augmentation Methods** & **SL** & **SCL** \\ \hline Random Horizontal Flip & & \\ Random Vertical Flip & & \\ Random Vertical Flip & & \\ Random Rotation (15 degree) & & \\ Color Jitter & & \\ \hline Random Horizontal Flip & & \\ Random Vertical Flip & & \\ Random Horizontal Flip & & \\ Random Rotation (15 degree) & & \\ \hline Random Horizontal Flip & & \\ Color Jitter & & \\ \hline Random Vertical Flip & & \\ Random Rotation (15 degree) & & \\ \hline Random Vertical Flip & & \\ Color Jitter & & \\ \hline Random Rotation (15 degree) & & \\ Color Jitter & & \\ \hline Random Horizontal Flip & & \\ Random Vertical Flip & & \\ Random Rotation (5 degree) & & \\ Color Jitter & & \\ \hline Random Horizontal Flip & & \\ Random Vertical Flip & & \\ Random Rotation (25 degree) & & \\ Color Jitter & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of different data augmentation methods.