Real-time gastric polyp detection using convolutional neural networks

• Published in: PloS one Vol. 14; no. 3; p. e0214133
• Main Authors: Zhang, Xu, Chen, Fei, Yu, Tao, An, Jiye, Huang, Zhengxing, Liu, Jiquan, Hu, Weiling, Wang, Liangjing, Duan, Huilong, Si, Jianmin
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25.03.2019; Public Library of Science (PLoS)
• Subjects: Adenomatous Polyps - diagnostic imaging; Analysis; Architectural engineering; Artificial neural networks; Biology and Life Sciences; Biomedical engineering; Colonoscopy; Computer and Information Sciences; Detection equipment; Education; Endoscopy; Engineering; Feature maps; Female; Frames per second; Gastric cancer; Gastroenterology; Gastroscopy; Hospitals; Humans; Image Processing, Computer-Assisted; International conferences; Laboratories; Male; Medical imaging; Medical imaging equipment; Medical personnel; Medical schools; Medicine and Health Sciences; Methods; Neural networks; Neural Networks, Computer; Pattern recognition; People and Places; Physical Sciences; Physicians; Polyps; R&D; Real time; Research & development; Research and Analysis Methods; Reuse; Stomach cancer; Stomach Neoplasms - diagnostic imaging; China

Computer-aided polyp detection in gastric gastroscopy has been the subject of research over the past few decades. However, despite significant advances, automatic polyp detection in real time is still an unsolved problem. In this paper, we report on a convolutional neural network (CNN) for polyp det...