Background We previously reported for the first time the usefulness of artificial intelligence
(AI) systems in detecting gastric cancers. However, the “original convolutional neural …

Artificial intelligence (AI) is gradually being utilized in various fields as its performance has
been improving with the development of deep learning methods, availability of big data, and …

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

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 …

Image recognition using artificial intelligence (AI) has progressed significantly due to
innovative technologies such as machine learning and deep learning. In the field of gastric …

The landscape of gastrointestinal endoscopy continues to evolve as new technologies and
techniques become available. The advent of image-enhanced and magnifying endoscopies …

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

Background Gastric cancer is a common kind of malignancies, with yearly occurrences
exceeding one million worldwide in 2017. Typically, ulcerous and cancerous tissues …

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 …