Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Dec 28, 2024; 30(48): 5111-5129
Published online Dec 28, 2024. doi: 10.3748/wjg.v30.i48.5111
Image detection method for multi-category lesions in wireless capsule endoscopy based on deep learning models
Zhi-Guo Xiao, Xian-Qing Chen, Dong Zhang, Xin-Yuan Li, Wen-Xin Dai, Wen-Hui Liang
Zhi-Guo Xiao, Xian-Qing Chen, Dong Zhang, Xin-Yuan Li, Wen-Xin Dai, Wen-Hui Liang, School of Computer Science Technology, Changchun University, Changchun 130022, Jilin Province, China
Zhi-Guo Xiao, School of Computer Science Technology, Beijing Institute of Technology, Beijing 100811, China
Author contributions: Xiao ZG and Chen XQ designed the research and wrote the manuscript; Zhang D and Dai WX collected and analyzed the data; Li XY and Liang WH performed data processing; All authors revised the manuscript and approved the final manuscript.
Supported by The Science and Technology Development Center of The Ministry of Education, No. 2022BC004.
Institutional review board statement: This study was approved by the Ethics Committee of the Affiliated Hospital of Changchun University, No. CCU2023043105.
Informed consent statement: The need for informed consent was waived owing to the retrospective nature of the study.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The datasets during the current study are not publicly available due to patient privacy and copyright issues but are available from the corresponding author upon reasonable request at 3220215169@bit.edu.cn.
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/
Corresponding author: Zhi-Guo Xiao, PhD, Additional Professor, School of Computer Science Technology, Changchun University, No. 6543 Weixing Road, Chaoyang District, Changchun 130022, Jilin Province, China. 3220215169@bit.edu.cn
Received: June 2, 2024
Revised: September 8, 2024
Accepted: October 8, 2024
Published online: December 28, 2024
Processing time: 179 Days and 21 Hours
Core Tip

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.