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 Aims This study aimed to evaluate the accuracy and effectiveness of the
convolutional neural network (CNN) in diagnosing gastric cancer and predicting the …

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 …

The identification of diseases is inseparable from artificial intelligence. As an important
branch of artificial intelligence, convolutional neural networks play an important role in the …

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 …

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

Abstract Background and Aim Recently, artificial intelligence (AI) has been used in
endoscopic examination and is expected to help in endoscopic diagnosis. We evaluated the …

Background Diagnosing early gastric cancer based on endoscopic findings requires
training, is time-consuming and difficult, and may produce false-negative results depending …

Background Early gastric cancer (EGC) has a high survival rate, but it is difficult to diagnosis.
Recently, artificial intelligence (AI) based on deep convolutional neural network (DCNN) has …

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 …