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

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 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 …

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

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 …

The early detection and accurate histopathological diagnosis of gastric cancer increase the
chances of successful treatment. The worldwide shortage of pathologists offers a unique …

Gastrointestinal endoscopy is widely conducted for the early detection of gastric cancer.
However, it is often difficult to detect early gastric cancer lesions and accurately evaluate the …

Background and Aims According to guidelines, endoscopic resection should only be
performed for patients whose early gastric cancer invasion depth is within the mucosa or …