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Artif Intell Gastrointest Endosc. Apr 28, 2021; 2(2): 12-24
Published online Apr 28, 2021. doi: 10.37126/aige.v2.i2.12
Application of deep learning in image recognition and diagnosis of gastric cancer
Yu Li, Da Zhou, Tao-Tao Liu, Xi-Zhong Shen
Yu Li, Da Zhou, Tao-Tao Liu, Xi-Zhong Shen, Department of Gastroenterology and Hepatology, Zhongshan Hospital Affiliated to Fudan University, Shanghai 200032, China
Author contributions: TT Liu and D Zhou contributed equally to conceptual development and supervision; Y Li and D Zhou contributed to the data collection and manuscript; XZ Shen supervised the paper; All authors have read and approved the final manuscript.
Supported by National Natural Science Foundation of China, No. 81800510; Shanghai Sailing Program, No. 18YF1415900.
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: Da Zhou, PhD, Doctor, Research Fellow, Department of Gastroenterology and Hepatology, Zhongshan Hospital Affiliated to Fudan University, 180 Feng Lin road, Shanghai 200032, China. mubing2007@foxmail.com
Received: February 15, 2021
Peer-review started: February 15, 2021
First decision: March 16, 2021
Revised: March 30, 2021
Accepted: April 20, 2021
Article in press: April 20, 2021
Published online: April 28, 2021
Processing time: 72 Days and 9.3 Hours
Core Tip

Core Tip: Gastric cancer is a life-threatening disease with a high mortality rate. With the development of deep learning in the image processing of gastrointestinal endoscope, the efficiency and accuracy of gastric cancer diagnosis through imaging technology have been greatly improved. At present, there is no comprehensive summary on the graphic recognition method for gastric cancer based on deep learning. In this review, some gastric cancer image databases and mainstream gastric cancer recognition models were summarized to make a prospect for the application of deep learning in this field.