## 3 Results  
We propose a classification method for normal and abnormal gastroscopy images through CADx. Figure 9a presents the architecture of a basic deep-learning model using a  
Figure 8: Process of the image quality measurement method. dataset consisting of only real gastroscopy images. Figure 9b presents the architecture of a deep-learning model using training data that combines DCGAN and AutoAugment. In a previous study [20], we applied 25 augmentation policies to optimize gastroscopy images in the CIFAR-10, ImageNet, and SVHN datasets. It was confirmed that the policies of the CIFAR-10 dataset are the most suitable for gastroscopy image classification. Therefore, in this study, the existing data were augmented 25 times by applying the optimized augmentation policies of the CIFAR-10 dataset. The augmentation policies--Equalize, AutoContrast, Color, and Brightness--were generally selected, and most of them chose color-based conversion. Moreover, we found that the results of Xception in the gastroscopy medical image classification were the best among four different deep-learning models, namely Xception, Inception-V3, Resnet-101, and Inception-Resnet-V2. Based on the results, we selected the Xception network for this study. The DCGAN and the 25 augmentation policies of the CIFAR-10 dataset were implemented to augment the training data. After selecting data using the Xception-based image quality-measurement method, the augmented data were trained and tested. Among the collected training data, 655 normal and 655 abnormal images were used to generate 200 normal and 200 abnormal images through the DCGAN, which were increased by 25 times through the CIFAR-10 dataset, and a total of 11,744 normal and 11,744 abnormal images were selected. The selection criterion of the data was an accuracy of at least 0.8. To verify whether the image quality-measurement method is effective, we compared the performances of the model with and without the image quality-measurement method. We used the receiver operating characteristic curve (ROC curve), an evaluation index, and compared the performances with the Az value of the area under the curve. Figure 10 presents the results of the classification performance of the models based on the ROC curve. As shown in Figure 10b, the Az values after adding 400 images through the DCGAN and after adding 23,488 images through the CIFAR-10 dataset were 0.882 and 0.884, respectively. After applying both DCGAN and CIFAR-10 datasets, the Az value was 0.9, which was the highest. The Az value was significantly (\(p\leq\) 0.01) higher for the DCGAN + CIFAR-10 than for the DCGAN and CIFAR-10. However, the difference between the DCGAN+CIFAR-10 and the original did not reach statistical significance (\(p\geq\) 0.01). The results confirmed that when data were augmented, the performance of all the data was improved, compared to the original data. Moreover, the model with the image quality-measurement method outperformed the model without the image quality-measurement method. Figure 9: Structure of the gastroscopy image classification model: (**a**) original and (**b**) DCGAN and 25 augmentation policies of the CIFAR-10 dataset. In addition, after making correct predictions for normal and abnormal images based on the confusion matrix, the performance was improved in terms of the accuracy, precision, recall, and F1 score, as computed using Equations (4)-(7).