Investigating Effective Data Augmentation Techniques for Accurate Gastric Classification in the Development of a Deep Learning-Based Computer-Aided Diagnosis System  
[PERSON]  
1 Department Graduate Program for BIT Medical Convergence, Kangwon National University,  
Chuncheon 24341, Republic of Korea 1  
[PERSON]  
1 Department Graduate Program for BIT Medical Convergence, Kangwon National University,  
Chuncheon 24341, Republic of Korea 1  
[PERSON]  
1 Department Graduate Program for BIT Medical Convergence, Kangwon National University,  
Chuncheon 24341, Republic of Korea 12 Department of Electronics Engineering, Kangwon National University, Chuncheon 24341, Republic of Korea 2  
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scans. However, with an increase in the number of people undergoing gastroscopy, the workload of specialists also increases, which may result in delays in the examination and biopsy periods. Numerous strategies and techniques have been investigated to improve the efficacy of medical procedures [4; 5; 6]. In particular, Computer-Aided Diagnosis (CADx), a system designed to support physicians in the diagnostic process across diverse clinical settings, has been a subject of extensive research [7; 8; 9]. It can improve the speed and accuracy of diagnoses by providing a consistent second opinion to doctors. Previous studies have demonstrated the practicality of deep learning, and further research is currently being conducted to optimize its performance and efficacy. [PERSON] et al. [10] and [PERSON] et al. [11] classified gastric cancers using the characteristics learned from gastric endoscopy histopathology slice images. They employed deep learning techniques to develop a model to distinguish between gastric cancer and noncancerous tissues. The results confirmed that CADx could accurately classify gastric cancer, achieving an accuracy of over 96% for slice-based classification. Thus, CADx can potentially improve the accuracy of diagnosis and early detection of gastric cancer, which is crucial for early treatment and improved patient outcomes. In addition to detection using gastric biopsy, detection that identifies the location of lesions using gastric endoscopic imaging has also been studied. [PERSON] et al. [12] conducted a study to detect gastric cancer lesions using gastroscopic imaging. They used data collected from two hospitals in Japan (Cancer Institute Hospital Ariake and Tokatsu-Tsujinaka Hospital) between April 2004 and December 2016, and 13,584 images of 2639 gastric cancer lesions were used as the training dataset. They employed a convolutional neural network (CNN) called the single-shot multibox detector (SSD) as the detection model. The developed CADx exhibited a sensitivity of 92.2% and could detect 70 of 71 lesions with a diameter of 6 mm or greater, as well as all invasive cancers. [PERSON] et al. [13] studied early gastric cancer (EGC) and invasion depth classifications. They collected data from 800 patients diagnosed with EGC at Gangnam Severance Hospital, Yonsei University College of Medicine in Seoul, South Korea, between January 2012 and March 2018. In total, 11,539 images (896 T1a-EGC, 809 T1b-EGC, and 9834 non-EGC images) were used in this study.