1114 artificial intelligence, trained with a rough binary classification, can select significant images of capsule endoscopy.

Since the introduction of computer vision technology using Deep-learning, various acceptable results have been reported for the recognition of small bowel pathologies in capsule endoscopy. However, the results are limitedly dealt with lesions such as erosions, ulcers, and angioectasia, which are easy to apply machine learning technologies. We classified capsule endoscopy images into those with and without significant lesions, and studied whether artificial intelligence, which learned the images of binary classification, can correctly suggest images containing significant lesions.
Source: Gastrointestinal Endoscopy - Category: Gastroenterology Authors: Tags: Oral abstracts Source Type: research