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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
Abstract

In recent years, artificial intelligence has been extensively applied in the diagnosis of gastric cancer based on medical imaging. In particular, using deep learning as one of the mainstream approaches in image processing has made remarkable progress. In this paper, we also provide a comprehensive literature survey using four electronic databases, PubMed, EMBASE, Web of Science, and Cochrane. The literature search is performed until November 2020. This article provides a summary of the existing algorithm of image recognition, reviews the available datasets used in gastric cancer diagnosis and the current trends in applications of deep learning theory in image recognition of gastric cancer. covers the theory of deep learning on endoscopic image recognition. We further evaluate the advantages and disadvantages of the current algorithms and summarize the characteristics of the existing image datasets, then combined with the latest progress in deep learning theory, and propose suggestions on the applications of optimization algorithms. Based on the existing research and application, the label, quantity, size, resolutions, and other aspects of the image dataset are also discussed. The future developments of this field are analyzed from two perspectives including algorithm optimization and data support, aiming to improve the diagnosis accuracy and reduce the risk of misdiagnosis.

Keywords: Endoscope; Artificial intelligence; Algorithm optimization; Data support

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.