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

Background Deep learning models have previously been established to predict the
histopathology and invasion depth of gastric lesions using endoscopic images. This study …

Background Gastric cancer is the third most lethal malignancy worldwide. A novel deep
convolution neural network (DCNN) to perform visual tasks has been recently developed …

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 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 …

Purpose: Gastric cancer remains the leading cause of cancer-related deaths in Northeast
Asia. Population-based endoscopic screenings in the region have yielded successful results …

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

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 …

Endoscopic image diagnosis assisted by machine learning is useful for reducing
misdetection and interobserver variability. Although many results have been reported, few …

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