Background and Aims Deep learning is an innovative algorithm based on neural networks.
Wireless capsule endoscopy (WCE) is considered the criterion standard for detecting small …

Gastrointestinal (GI) disease is one of the most common diseases and primarily examined
by GI endoscopy. Recently, deep learning (DL), in particular convolutional neural networks …

Background and Aims The aim of our study was to develop and evaluate a deep learning
algorithm for the automated detection of small-bowel ulcers in Crohn's disease (CD) on …

A novel computer-aided detection method based on deep learning framework was proposed
to detect small intestinal ulcer and erosion in wireless capsule endoscopy (WCE) images. To …

Wireless capsule endoscopy (WCE) is one of the most efficient methods for the examination
of gastrointestinal tracts. Computer-aided intelligent diagnostic tools alleviate the challenges …

Wireless capsule endoscopy is a non-invasive, wireless imaging tool that has developed
rapidly over the last several years. One of the main limiting factors using this technology is …

BACKGROUND Artificial intelligence, such as convolutional neural networks (CNNs), has
been used in the interpretation of images and the diagnosis of hepatocellular cancer (HCC) …

Biomedical devices provide a critical role in the healthcare system to positively impact
patient well-being. This paper aims to provide the current classifications and …

Wireless capsule endoscopy (WCE) is a process in which a patient swallows a camera-
embedded pill-shaped device that passes through the gastrointestinal (GI) tract, captures …

Precise and efficient automated identification of gastrointestinal (GI) tract diseases can help
doctors treat more patients and improve the rate of disease detection and identification …