Feng XY, Xu X, Zhang Y, Xu YM, She Q, Deng B. Application of convolutional neural network in detecting and classifying gastric cancer. Artif Intell Gastrointest Endosc 2021; 2(3): 71-78 [DOI: 10.37126/aige.v2.i3.71]
Corresponding Author of This Article
Bin Deng, MD, Associate Professor, Chief Physician, Department of Gastroenterology, Affiliated Hospital of Yangzhou University, No. 368 Hanjiang Middle Road, Yangzhou 225000, Jiangsu Province, China. chinadbin@126.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Artif Intell Gastrointest Endosc. Jun 28, 2021; 2(3): 71-78 Published online Jun 28, 2021. doi: 10.37126/aige.v2.i3.71
Application of convolutional neural network in detecting and classifying gastric cancer
Xin-Yi Feng, Xi Xu, Yun Zhang, Ye-Min Xu, Qiang She, Bin Deng
Xin-Yi Feng, Xi Xu, Yun Zhang, Ye-Min Xu, Qiang She, Bin Deng, Department of Gastroenterology, Affiliated Hospital of Yangzhou University, Yangzhou 225000, Jiangsu Province, China
Author contributions: Feng XY and Xu X contributed equally to this work; Feng XY and Xu X conceived and drafted the manuscript; Feng XY, Xu X, Zhang Y, and Xu YM collected the relevant information; She Q and Deng B revised the manuscript.
Supported byThe Key Project for Social Development of Yangzhou, No. YZ2020069.
Conflict-of-interest statement: The authors report no conflicts of interest in this work.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Bin Deng, MD, Associate Professor, Chief Physician, Department of Gastroenterology, Affiliated Hospital of Yangzhou University, No. 368 Hanjiang Middle Road, Yangzhou 225000, Jiangsu Province, China. chinadbin@126.com
Received: April 27, 2021 Peer-review started: April 27, 2021 First decision: April 28, 2021 Revised: May 21, 2021 Accepted: June 7, 2021 Article in press: June 7, 2021 Published online: June 28, 2021 Processing time: 70 Days and 3.8 Hours
Abstract
Gastric cancer (GC) is the fifth most common cancer in the world, and at present, esophagogastroduodenoscopy is recognized as an acceptable method for the screening and monitoring of GC. Convolutional neural networks (CNNs) are a type of deep learning model and have been widely used for image analysis. This paper reviews the application and prospects of CNNs in detecting and classifying GC, aiming to introduce a computer-aided diagnosis system and to provide evidence for subsequent studies.
Core Tip: With the development of new algorithms and big data, great achievements in artificial intelligence (AI) based on deep learning have been made in diagnostic imaging, especially convolutional neural network (CNN). Esophagogastroduodenoscopy (EGD) is currently the most common method for screening and diagnosing gastric cancer (GC). When AI was combined with EGD, the diagnostic efficacy of GC could be improved. Therefore, we review the application and prospect of CNN in detecting and classifying GC, aiming to introduce a computer-aided diagnosis system and provide evidence for following studies.
