Background and Aims A deep convolutional neural network (CNN) was used to achieve fast
and accurate artificial intelligence (AI)‐assisted diagnosis of early gastric cancer (GC) and …

Diagnosis and evaluation of early gastric cancer (EGC) using endoscopic images is
significantly important; however, it has some limitations. In several studies, the application of …

Background Image recognition using artificial intelligence with deep learning through
convolutional neural networks (CNNs) has dramatically improved and been increasingly …

Objectives Detecting early gastric cancer is difficult, and it may even be overlooked by
experienced endoscopists. Recently, artificial intelligence based on deep learning through …

Simple Summary Gastric cancer (GC) is one of the most newly diagnosed cancers and the
fifth leading cause of death globally. Previous studies reported that the detection rate of …

Background and study aims Recently, a growing body of evidence has been amassed on
evaluation of artificial intelligence (AI) known as deep learning in computer-aided diagnosis …

Background and Aims Endoscopic differential diagnoses of gastric mucosal lesions (benign
gastric ulcer, early gastric cancer [EGC], and advanced gastric cancer) remain challenging …

Background Visual inspection, lesion detection, and differentiation between malignant and
benign features are key aspects of an endoscopist's role. The use of machine learning for …

BACKGROUND: A timely diagnosis of early gastric cancer (EGC) can greatly reduce the
death rate of patients. However, the manual detection of EGC is a costly and low-accuracy …

This study developed a new convolutional neural network model to detect and classify
gastric lesions as malignant, premalignant, and benign. We used 10,181 white-light …