## 5 Discussion  
Experiments were conducted to compare the performance of representative CNN-based models (VGGNet, GoogLeNet, and ResNet) and ResNet models with varying layer depths using the same DBs. The number of layers applied in each model varies from 11 for VGGNet, 22 for GoogLeNet, 18 for ResNet18, 34 for ResNet34, and 50 for ResNet50. The comparison results shown in Table 2 confirm that the number of layers applied in models did not affect their performance. Additionally, the results show that ResNet18 outperformed GoogLeNet, and both ResNet18 and ResNet34 demonstrated superior performance compared to ResNet50. For comparison purposes, models were selected based on [8]. Additionally, it was observed that the training of ResNet18 and ResNet34 resulted in overfitting, as depicted in Figure 6. A study [6] highlights that model training based on a small-scale dataset may cause overfitting issues. Taking this phenomenon into account, this study compared models using Original DB-A which comprised 5596 samples, and Original DB-T comprising 11,192 samples. The comparison, as illustrated in Table 5, verified that an increase in the dataset size improved model performance. Furthermore, when data augmentation was applied to Original DB-A using the DA-S technique proposed in this study, model performances were further enhanced. Tables 4 and 5 indicate that applying the DA-S technique to Original DB-A for data augmentation yielded better model performance compared to combining Original DB-B with Original DB-A to increase the dataset size. These findings align with a study [6], suggesting that model performances can be enhanced through both increasing the dataset size and diversifying images. The comparison results presented in Table 5 demonstrate that the enhancement of model performance was achieved not only by increasing the dataset size but also by applying the DA-S technique. Figure 7 presents graphs illustrating the mean accuracies of models, comparing the overfitting tendencies observed when using Original DB-T with those observed when using the DB augmented with the DA-S technique. Figure 7a depicts the mean accuracies of a model trained with Original DB-T. As is evident from this graph, overfitting occurred due to the model's excessive adaptation to the training data. Figure 7b depicts the mean accuracies of a model trained with the DB augmented with the DA-S technique. This graph demonstrates a decrease in overfitting compared to that observed in Figure 7a. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & & \multicolumn{2}{c}{**Mean Accuracy [\%]**} \\ \cline{2-4}  & **DAS 0\% + DAS** & **DAS 0\% + DAS** & **DAS 99.49\% +** \\  & **99.49\%** & **99.85\%** & **DAS 99.85\%** \\  & **(\(\mathbf{w}\) = 0, 2)** & **(\(\mathbf{w}\) = 0, 3)** & **(\(\mathbf{w}\) = 2, 3)** \\ \hline Number of data & 33,576 & 33,576 & 33,576 \\ ResNet18 & 91.15 & 89.03 & 89.53 \\ ResNet34 & 91.02 & 89.15 & 89.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of accuracies of models depending on saturation weight combinations. In Table 6, ResNet18 and ResNet34 demonstrated the highest mean accuracies of 91.15% and 91.02%, respectively, when combining saturation weights 0 and 2. When a combination of saturation weights 2 and 3 was applied, these models exhibited the second-highest mean accuracies of 89.53% and 89.79%, respectively. When a combination of saturation weights 0 and 3 was applied, these models exhibited the lowest mean accuracies of 89.03% and 89.15%, respectively. These results indicate that saturation weight 2 was included in the combinations of saturation weights that led to high mean accuracies of models. Based on these results, it is assumed that the saturation weight 2 is associated with the enhancement of model performance. Saturation is the equivalent of color jitter among data enhancement techniques, which refers to the process of randomly changing the brightness, color, saturation, etc. of an image.