Background This study was aimed to develop a computer-aided diagnosis (CAD) system
with deep-learning technique and to validate its efficiency on detecting the four categories of …

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 …

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 …

Gastrointestinal endoscopy is the primary method used for the diagnosis and treatment of
gastric polyps. The early detection and removal of polyps is vitally important in preventing …

Gastrointestinal (GI) cancers, mainly including esophageal cancer, gastric cancer, and
colorectal cancer, are the leading cause of cancer-related deaths worldwide. Most GI …

The human gastrointestinal (GI) system can be affected by various illnesses which results in
the death of about two million patients globally. Endoscopy helps to detect such diseases as …

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

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 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 …

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 …