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Articles Published Processes
10/13/2026 9:29:23 AM | Browse: 1 | Download: 2
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Received |
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2026-08-20 02:54 |
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Peer-Review Started |
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2026-08-20 02:54 |
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First Decision by Editorial Office Director |
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Return for Revision |
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2026-09-10 03:36 |
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Revised |
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2026-09-18 06:38 |
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Publication Fee Transferred |
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2026-09-30 08:52 |
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Second Decision by Editor |
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2026-10-13 02:58 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-10-13 02:59 |
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Articles in Press |
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2026-10-13 02:59 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-10-13 04:06 |
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Publish the Manuscript Online |
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2026-10-13 09:29 |
| ISSN |
2307-8960 (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 |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| 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 |
Retrospective Study |
| Article Title |
Deep self-organizing competitive neural network for magnetically controlled capsule endoscopy-based auxiliary diagnosis of clinically significant gastric lesions
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Ying-Ying Song, Chun-Mei Wang, Zhao-Yu Chen, Peng-Jie Xu and Jing Zhang |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Shanghai Municipal Health Commission Seed Program for Medical New Technology Research and Translation |
2024ZZ2048 |
| Public Sector Research Project in Fengxian District, Shanghai |
FXKX-2025-006 |
| Zhejiang University Education Foundation Project |
2025-KY-301(K) |
| Engineering Research Center for Big Data Application in Private Health Medicine of Fujian Universities, Putian University, Putian, Fujian 351100, China |
MKF202406 |
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| Corresponding Author |
Jing Zhang, Department of Gastroenterology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600 Yishan Road, Shanghai 200233, China. jing5522724@vip.163.com |
| Key Words |
Auxiliary diagnosis; Magnetically controlled capsule endoscopy; Deep self-organizing competitive neural network; Text convolutional neural network; Clinically significant gastric lesions |
| Core Tip |
Magnetically controlled capsule endoscopy (MCCE) is a well-tolerated alternative to invasive gastroscopy, but its diagnostic efficiency and consistency are limited by large data volume and physician-dependent diagnosis. This study constructs a deep self-organizing competitive neural network (DSomNN) to enhance MCCE-based diagnostic accuracy and efficiency, and the DSomNN performs well in gastric cancer risk prediction, addresses MCCE limitations, and serves as a valuable auxiliary tool for clinical gastroenterology decision-making. |
| Publish Date |
2026-10-13 09:29 |
| Citation |
Song YY, Wang CM, Chen ZY, Xu PJ, Zhang J. Deep self-organizing competitive neural network for magnetically controlled capsule endoscopy-based auxiliary diagnosis of clinically significant gastric lesions. World J Clin Cases 2026; 14(27): 126704
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| URL |
https://www.wjgnet.com/2307-8960/full/v14/i27/126704.htm |
| DOI |
https://doi.org/10.12998/wjcc.126704 |
Copyright © 1993-2026 Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA. All rights reserved, including rights relating to text and data mining, AI training, and similar technologies. For open-access content, the applicable copyright and licensing terms govern.
