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4/28/2021 2:31:45 PM | Browse: 398 | Download: 740
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Received |
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2021-02-15 03:22 |
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Peer-Review Started |
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2021-02-15 03:24 |
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To Make the First Decision |
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Return for Revision |
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2021-03-19 15:06 |
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Revised |
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2021-03-30 05:13 |
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Second Decision |
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2021-04-19 06:46 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2021-04-20 05:09 |
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Articles in Press |
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2021-04-20 05:09 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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2021-04-28 01:25 |
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Typeset the Manuscript |
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2021-04-28 09:02 |
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Publish the Manuscript Online |
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2021-04-28 12:41 |
| ISSN |
2689-7164 (online) |
| 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/ |
| Copyright |
© The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Minireviews |
| Article Title |
Application of deep learning in image recognition and diagnosis of gastric cancer
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Yu Li, Da Zhou, Tao-Tao Liu and Xi-Zhong Shen |
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National Natural Science Foundation of China |
81800510 |
| Shanghai Sailing Program |
18YF1415900 |
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| Corresponding Author |
Da Zhou, PhD, Doctor, Doctor, Research Fellow, Department of Gastroenterology and Hepatology, Zhongshan Hospital Affiliated to Fudan University, 180 Feng Lin road, Shanghai 200032, China. mubing2007@foxmail.com |
| Key Words |
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. |
| Publish Date |
2021-04-28 12:41 |
| Citation |
Li Y, Zhou D, Liu TT, Shen XZ. Application of deep learning in image recognition and diagnosis of gastric cancer. Artif Intell Gastrointest Endosc 2021; 2(2): 12-24 |
| URL |
https://www.wjgnet.com/2689-7164/full/v2/i2/12.htm |
| DOI |
https://dx.doi.org/10.37126/aige.v2.i2.12 |
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