## 3 Results  
This study aimed to improve the accuracy of gastroscopic classification using a gastric endoscopic image augmentation policy. This effect was confirmed using two datasets. The collected T-stage 1 and T-stage 4 data were used as training (1024 images), validation (385 images), and test (404 images) data. Further, the collected EGC and normal data were used as training (360 images), validation (120 images), and test (120 images) data. AutoAugment generated 25 times more training data images. The lesions were pasted onto normal images using a sliding window algorithm for the Cut-and-Paste technique. The detailed composition of the dataset is presented in Table 3. T-stage 1 and T-stage 4 datasets were defined as Dataset A, and the EGC and normal datasets were defined as Dataset B.  
The EfficientNetV2-L model was used to evaluate classification network performance. The performance evaluation indices of the trained model were as follows: (1) precision, which is the proportion of predictions that are actually true; (2) sensitivity, which represents the proportion of predictions that are true among those that are actually true; (3) the False Positive Rate (FPR) represents the proportion of true negatives that were inaccurately classified as positives; (4) the F1-score represents the harmonic average of precision and sensitivity; and (5) accuracy, which is the percentage of all predictions that were correctly predicted. The performance of the model for each dataset is summarized in Table 4.  
Early diagnosis of gastric cancer is essential for improving treatment outcomes and prognosis. For this reason, sensitivity, representing the classification accuracy of gastric cancer images, is a crucial evaluation metric in this study. The results showed that the classification sensitivity using Dataset A improved by 6.5%, from 0.7655 to 0.8312, and that using Dataset B improved by 9.1%, from 0.7500 to 0.8417. The accuracy also exhibited a similar improvement to the sensitivity. The classification accuracy using Dataset A improved by 6.6%, from 0.7649 to 0.8317, and that using Dataset B improved by 9.1%, from 0.7500 to 0.8417.  
To address the issue of the limited test dataset in Dataset B, TTA was applied. Consequently, the obtained outcomes were evaluated using a broader data range, which can be considered as an evaluation of enhanced generalization. Table 5 presents the results obtained by applying the TTA algorithm to Dataset B.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Metrics** & **Original** & **AutoAugment** & **Cut and Paste** \\ \hline \multirow{4}{*}{Dataset A} & Precision & 0.7658 & 0.7936 & 0.8320 \\  & Sensitivity & 0.7655 & 0.7929 & 0.8312 \\  & FPR & 0.2113 & 0.1780 & 0.1737 \\  & F1-score & 0.7656 & 0.7933 & 0.8316 \\  & Accuracy & 0.7649 & 0.7921 & 0.8317 \\ \hline \multirow{4}{*}{Dataset B} & Precision & 0.7747 & 0.8114 & 0.8715 \\  & Sensitivity & 0.7500 & 0.7833 & 0.8417 \\ \cline{1-1}  & FPR & 0.3077 & 0.2821 & 0.2338 \\ \cline{1-1}  & F1-score & 0.7622 & 0.7971 & 0.8563 \\ \cline{1-1}  & Accuracy & 0.7500 & 0.7833 & 0.8417 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Gastric cancer classification results by data augmentation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Dataset A**} & \multicolumn{2}{c}{**Dataset B**} \\ \cline{2-5}  & **T-Stage 1** & **T-Stage 4** & **EGC** & **NOR** \\ \hline Original & 512 & 512 & 180 & 180 \\ AutoAugment & 13,312 & 13,312 & 4680 & 4680 \\ Cut and Paste & 166,912 & 166,912 & 37,080 & 4680 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Configuration of augmented gastric cancer training datasets.  
The application of the TTA algorithm to Dataset B resulted in an increase in sensitivity compared to when TTA was not applied. Specifically, the sensitivity increased by 2.2% for the original model, 8.3% for the model with only AutoAugment, and 5.8% for the model with Cut-and-Paste augmentation.