## 1 Introduction  
According to the statistics released by the Global Cancer Observatory in 2018, gastric cancer is the fifth most frequently diagnosed cancer and the third leading cause of cancer deaths worldwide, as shown in Figure 1[1]. To increase the survival rate of patients with gastric cancer, it is important to detect and treat it early through gastroscopy. A previous study showed that the survival rate of patients with gastric cancer who underwent gastric endoscopy was 2.24 times higher than that of those who did not [2]. Precancerous lesions that cause gastric cancer include gastritis, gastric ulcer, and gastric bleeding. Most of these gastric diseases are difficult to detect because they are asymptomatic until they develop into gastric cancer. Therefore, gastric cancer can be prevented through the early detection of lesions that develop into gastric cancer with regular gastroscopy. As the importance of gastroscopy increases, the number of gastroscopy examineees is expected to increase. In addition, as the imaging technology continually develops and the number of medical images rapidly increases,the fatigue experienced by specialists who perform the diagnosis by relying on the naked eye increases, and differences in diagnosis occur depending on the skill of the specialist. Therefore, the need for a computer-aided diagnosis (CADx) system to assist specialists in performing the diagnosis is increasing. A CADx system assists a doctor in performing the diagnosis by detecting and analyzing lesions, reduces the manual work of endoscopy specialists, and improves the accuracy of gastric-disease diagnosis. Currently, CADx systems using deep learning are being actively studied. According to research results, the latest deep-learning technologies can consistently deliver superior performance even if the data contain some errors, as long as there are enough data [3]. However, if the system does not have enough training data to train parameters, then overfitting problems that hinder its performance occur more easily. Therefore, deep learning-based research requires a large amount of quality data. However, the collection of medical data, owing to their nature, requires approval from institutional review boards for the protection of personal information of patients; further, it is highly expensive and time consuming because of the involved verification process that includes medical examinations and biopsy tests. To solve this problem, many studies have proposed methods of generating data similar to actual data through data augmentation. [PERSON] et al. [4] introduced data-augmentation methods through rotations, flipping, transmission, and scaling and a general adversarial network (GAN) for computed-tomography (CT) images of the liver. [PERSON] et al. [5] proposed a method of augmenting data and classifying melanoma by applying random image-brightness and color-contrast values to skin-lesion images. [PERSON] et al. [6] proposed a method of generating synthetic abnormal magnetic resonance imaging (MRI) images with brain tumors using a GAN from brain MRI images. [PERSON] et al. [7] produced split images of the lungs and heart through a trained GAN from chest X-ray images. [PERSON] et al. [8] improved the performance of the method classifying malignant and benign pulmonary nodules by generating various lung CT images through a forward GAN and improving the image quality through a backward GAN. In addition, [PERSON] et al. [9] flipped, rotated, and blurred thyroid nodule ultrasound images to augment data, extract features, and classify them as either benign or malignant through a random forest classifier. [PERSON] et al. [10] classified different types of lung nodule malignancies through a multilevel cross-residual convolutional neural network (CNN). These studies [4; 5; 6; 7; 8; 9; 10] were conducted by applying a basic augmentation method or GAN for lesions such as lung, skin, and brain lesions but not gastric lesions. [PERSON] et al.