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Publication Name Artificial Intelligence in Gastrointestinal Endoscopy
Manuscript ID 67549
Country/Territory China
Category Gastroenterology and Hepatology
Manuscript Type Minireviews
Article Title Application of a convolutional neural network in detecting and classifying gastric cancer
Manuscript Source Invited Manuscript
All Author List Xin-Yi Feng, Xi Xu, Yun Zhang, Ye-Min Xu, Qiang She and Bin Deng
Funding Agency and Grant Number
Funding Agency Grant Number
The Key Project for Social development of Yangzhou YZ2020069
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
Key Words Artificial intelligence; Convolutional neural network; Endoscopy; Gastric cancer; Deep learning
Core Tip With the development of new algorithms and big data, great achievements of 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 evidences for following researches.
Citation Feng XY, Xu X, Zhang Y, Xu YM, She Q, Deng B. Application of a convolutional neural network in detecting and classifying gastric cancer. Artif Intell Gastrointest Endosc 2021; In press
Received
2021-04-27 06:14
Peer-Review Started
2021-04-27 06:15
To Make the First Decision
Return for Revision
2021-04-28 21:50
Revised
2021-05-21 13:02
Second Decision
2021-06-07 12:28
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief
2021-06-07 13:25
Articles in Press
2021-06-07 13:25
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript
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.
Permissions For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website http://www.wjgnet.com
Assistant Editor Assistant Editor
Science Editor Chen-Chen Gao
Reviewer Code 04743986
Editorial Director
Journal Chief Editor
Company Chief Editor Ze-Mao Gong
Language Editor
Production Editor
Responsible Production Editor
Responsible Science Editor
Proofing Editorial Director Jin-Lei Wang
Proofing Editor-in-Chief Lian-Sheng Ma
Manuscript File 67549_Auto_Edited-ZMG.docx
Answering Reviewers 67549-Answering reviewers.pdf
Audio Core Tip 67549-Audio core tip.wav
Conflict-of-Interest Disclosure Form 67549-Conflict-of-interest statement.pdf
Copyright License Agreement 67549-Copyright license agreement.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s) 67549-Grant application form(s).pdf
Non-Native Speakers of English Editing Certificate 67549-Language certificate.pdf
Peer-review Report 67549-Peer-review(s).pdf
Scientific Misconduct Check 67549-Scientific misconduct check.pdf
Scientific Editor Work List 67549-Scientific editor work list.pdf