Anatomical sites recognition is a basic requirement for gastroenterologists. But there is not a
unified framework for anatomical sites identification in both ordinary and capsule …

Background: Gastric structure recognition systems have become increasingly necessary for
the accurate diagnosis of gastric lesions in capsule endoscopy. Deep learning, especially …

Purpose: The purpose of this paper is to develop a method to automatic classify capsule
gastroscope image into three categories to prevent high-risk factors for carcinogenesis, such …

The first step in reading a capsule endoscopy (CE) is determining the gastrointestinal (GI)
organ. Because CE produces too many inappropriate and repetitive images, automatic …

Methods–Our group developed a CNN-based algorithm for the automatic classification of
pleomorphic gastric lesions, including vascular lesions (angiectasia, varices and red spots) …

The use of convolutional neural networks (CNNs) has dramatically advanced our ability to
recognize images with machine learning methods. We aimed to construct a CNN that could …

This work provides a novel segmentation and classification of diseases from the GI tract
using WCE images. First, the input WCE images are collected from the KID Atlas data set …

Automated diagnosis of digestive tract diseases from gastrointestinal endoscopy images is
of high importance for improving the diagnosis accuracy and efficiency. The current …

Introduction Artificial intelligence (AI) that surpasses human ability in image recognition is
expected to be applied in the field of gastrointestinal endoscopes. Accordingly, its research …

Purpose Detection of gastrointestinal (GI) diseases involves several expensive, challenging,
and time-consuming procedures. Deep learning techniques-based computer-aided …