Publication Name	World Journal of Gastroenterology
Manuscript ID	97557
Country	China

Received	2024-06-02 12:59
Peer-Review Started	2024-06-02 12:59
To Make the First Decision
Return for Revision	2024-08-27 23:53
Revised	2024-09-08 11:55
Second Decision	2024-10-12 02:44
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-10-12 06:12
Articles in Press	2024-10-12 06:12
Publication Fee Transferred	2024-10-15 05:59
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-10-28 02:46
Publish the Manuscript Online	2024-11-29 08:40

ISSN	1007-9327 (print) and 2219-2840 (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: https://creativecommons.org/licenses/by-nc/4.0/
Copyright	© The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
Article Reprints	For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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

Category	Computer Science, Interdisciplinary Applications
Manuscript Type	Retrospective Study
Article Title	Image detection method for multi-category lesions in wireless capsule endoscopy based on deep learning models
Manuscript Source	Unsolicited Manuscript
All Author List	Zhi-Guo Xiao, Xian-Qing Chen, Dong Zhang, Xin-Yuan Li, Wen-Xin Dai and Wen-Hui Liang
Funding Agency and Grant Number	Funding Agency Grant Number The Science and Technology Development Center of The Ministry of Education 2022BC004
Corresponding Author	Zhi-Guo Xiao, PhD, School of Computer Science Technology, Changchun University, No. 6543 Weixing Road, Chaoyang District, Changchun 130022, Jilin Province, China. 3220215169@bit.edu.cn
Key Words	Human digestive tract; Artificial intelligence; Deep learning; Wireless capsule endoscopy; Object detection
Core Tip	In clinical practice, wireless capsule endoscopy is commonly used to detect lesions in the digestive tract and search for their causes. Here, we propose a multilesion classification and detection model to automatically identify 23 types of lesions in the digestive tract, and accurately mark the lesions. The model can improve the diagnostic efficiency of doctors and their ability to identify the categories of digestive tract lesions.
Publish Date	2024-11-29 08:40
Citation	<p>Xiao ZG, Chen XQ, Zhang D, Li XY, Dai WX, Liang WH. Image detection method for multi-category lesions in wireless capsule endoscopy based on deep learning models. <i>World J Gastroenterol</i> 2024; 30(48): 5111-5129</p>
URL	https://www.wjgnet.com/1007-9327/full/v30/i48/5111.htm
DOI	https://dx.doi.org/10.3748/wjg.v30.i48.5111

Full Article (PDF)	WJG-30-5111-with-cover.pdf
Manuscript File	97557_Auto_Edited_084638.docx
Answering Reviewers	97557-answering-reviewers.pdf
Audio Core Tip	97557-audio.mp3
Biostatistics Review Certificate	97557-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form	97557-conflict-of-interest-statement.pdf
Copyright License Agreement	97557-copyright-assignment.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	97557-foundation-statement.pdf
Signed Informed Consent Form(s) or Document(s)	97557-informed-consent-statement.pdf
Institutional Review Board Approval Form or Document	97557-institutional-review-board-statement.pdf
Non-Native Speakers of English Editing Certificate	97557-non-native-speakers.pdf
Peer-review Report	97557-peer-reviews.pdf
Scientific Misconduct Check	97557-scientific-misconduct-check.png
Scientific Editor Work List	97557-scientific-editor-work-list.pdf
CrossCheck Report	97557-crosscheck-report.png
CrossCheck Report	97557-crosscheck-report.pdf