Using VGG-16, they achieved detection accuracies of 0.981 for EGC and 0.851 for tumor depth classification. [PERSON] et al. [14] identified EGC using narrow-band magnification images. They  
Figure 1: Gastric cancer rates per 100,000 people by region. collected 1777 magnifying endoscopy with narrow-band imaging (ME-NBI) images from three hospitals in China (Endoscopic Center of Zhongshan Hospital, Affiliated Dongnan Hospital of Xiamen University, and Central Hospital of Wuhan) and used the VGG-19 architecture to identify EGC. The accuracies achieved in this study were 0.808 and 0.813, respectively, similar to those of senior endoscopists. [PERSON] et al. [15] conducted a study using 1492 cancer ME-NBI images and 1078 normal ME-NBI images collected between April 2005 and February 2016. They achieved an accuracy of 0.8684 and compared the results with those determined by 11 doctors for verification. They reported that the CADx system exhibited higher accuracy and sensitivity than those of the experts. However, in contrast to general endoscopes, ME-NBI has an optical system and an imaging system to increase resolution and sharpness, which slows the shooting speed and increases the inspection cost. Consequently, instead of ME-NBI, studies have been conducted using white light endoscopy to segment and classify benign gastric ulcer (BGU), EGC, and advanced gastric cancer (AGC) lesions and to classify T1a and T1b by the depth of invasion through normal gastric endoscopy images. [PERSON] et al. [16] conducted a study classifying gastric mucosal lesions, including BGU, EGC, and AGC, using normal endoscopic images. This study included 1366 patients from two hospitals in Korea (Seoul National University Hospital and Samsung Medical Center). They utilized a CNN model for gastric lesion classification and achieved internal and external verification accuracies of 0.923 and 0.813, respectively. Furthermore, they achieved an internal verification accuracy of 0.77 and an external verification accuracy of 0.72 with the depth of invasion classification performance for T1a and T1b. A common characteristic of previous studies is that the medical images contain personal patient information, which requires a long time for data collection, resulting in the construction of a small dataset. However, a small dataset size increases the risk of problems such as overfitting or underfitting in the model training owing to the possible requirement of more than one learning process. Data augmentation can ensure diversity in a dataset and facilitate the development of models with improved performance by learning the characteristics of various lesions. Consequently, this could enhance the accuracy and reliability of medical image analysis, ultimately leading to better patient conditions. A study was conducted to classify gastric cancers using data augmentation. [PERSON] et al. [17] studied patients with gastric tumors detected at Chuncheon Sacred Heart Hospital in Gangwon Province, South Korea, between 2010 and 2017. Eventually, 2899 images from 846 patients were included in this study. Among them, images of submuc-invaded lesions accounted for 34.5% (n = 999), and those of mucosa-confined lesions accounted for 65.5% (n = 1900). The mucosa-confined lesions were rotated by 90\({}^{\circ}\) to double their size and overcome data imbalance. The entire image dataset was then augmented four times by flipping it vertically and horizontally. Dense-Net-16 and Inception-ResNet-v2 were used for the training. Overall accuracies of 0.774 and 0.841 were achieved for internal validation, and accuracies of 0.661 and 0.714 were achieved when only classifying the invasion depth of EGC. Further, an external test dataset was constructed by collecting the images of consecutive patients who underwent gastric endoscopy between 2019 and 2020. Overall accuracies of 0.741 and 0.773 were achieved for external validation, and accuracies of 0.650 and 0.672 were achieved when only the invasion depth of the EGC was classified.