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 …

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

Background Using deep learning techniques in image analysis is a dynamically emerging
field. This study aims to use a convolutional neural network (CNN), a deep learning …

Background and Aims We aimed to develop and validate a deep learning–based system
that covers various aspects of early gastric cancer (EGC) diagnosis, including detecting …

Background We aimed to develop and validate a real-time deep convolutional neural
networks (DCNNs) system for detecting early gastric cancer (EGC). Methods All 45,240 …

Background and Aims The prognosis of esophageal cancer is relatively poor. Patients are
usually diagnosed at an advanced stage when it is often too late for effective treatment …

Background: Classification of colorectal neoplasms during colonoscopic examination is
important to avoid unnecessary endoscopic biopsy or resection. This study aimed to develop …

Background and Aims Narrow-band imaging with magnifying endoscopy (ME-NBI) has
shown advantages in the diagnosis of early gastric cancer (EGC). However, proficiency in …