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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Nov 7, 2025; 31(41): 111184
Published online Nov 7, 2025. doi: 10.3748/wjg.v31.i41.111184
Published online Nov 7, 2025. doi: 10.3748/wjg.v31.i41.111184
Assessing deep learning models for multi-class upper endoscopic disease segmentation: A comprehensive comparative study
In Neng Chan, Pak Kin Wong, Ye-Ying Qin, Department of Electromechanical Engineering, University of Macau, Macau 999078, China
Tao Yan, School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
Yan-Yan Hu, Zheng Li, Shan Gao, Department of Gastroenterology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, Hubei Pro
Chon In Chan, Ieng Hou Lam, Sio Hou Wong, Hon Ho Yu, Department of Gastroenterology, Kiang Wu Hospital, Macau 999078, China
Chi Hong Wong, In Weng Chan, Faculty of Medicine, Macau University of Science and Te
Liang Yao, Bao-Liang Zhao, Ying Hu, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
Author contributions: Chan IN and Wong PK conceptualized the study, contributed to methodology and resources, were responsible for writing the original draft, and contributed equally to this work; Wong PK and Yan T provided supervision, project administration, and funding acquisition; Chan IN developed the software and conducted the investigation; Yan T contributed to resources and critically revised the manuscript; Hu YY and Chan CI contributed to conceptualization, data curation, and resources; Qin YY conducted the investigation and contributed to writing, review, and editing; Wong CH and Chan IW contributed to data curation, review, and editing; Lam IH, Wong SH, and Li Z were responsible for data curation; Gao S, Yu HH, Yao L, Zhao BL, and Wu Y provided supervision; all authors read and approved the final version of the manuscript.
Supported by the Guangdong Basic and Applied Basic Research Foundation, No. 2021B1515130003; the Key Research and Development Plan of Hubei Province, No. 2022BCE034; and the Natural Science Foundation of Hubei Province, No. 2024AFB1054.
Institutional review board statement: The study was reviewed and approved by the Medical Ethics Committee of Xiangyang Central Hospital (No. 2024-145) and the Medical Ethics Committee of Kiang Wu Hospital, Macao (No. 2019-005), and conducted in accordance with the principles of the Declaration of Helsinki.
Institutional animal care and use committee statement: This study did not involve any animal experiments or the use of laboratory animals.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: The data of the self-collected dataset is available if requested, while the EDD2020 dataset is a public dataset that is freely accessible to researchers.
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: Pak Kin Wong, PhD, Professor, Department of Electromechanical Engineering, University of Macau, Avenida da Universidade, Taipa, Macau 999078, China.
Received: June 26, 2025
Revised: August 3, 2025
Accepted: September 28, 2025
Published online: November 7, 2025
Processing time: 134 Days and 4 Hours
Revised: August 3, 2025
Accepted: September 28, 2025
Published online: November 7, 2025
Processing time: 134 Days and 4 Hours
Core Tip
Core Tip: This study evaluates 17 advanced deep learning models, including convolutional neural network-, transformer-, and mamba-based architectures, for multi-class upper gastrointestinal disease segmentation. Swin-UMamba achieves the highest segmentation accuracy, while SegFormer balances efficiency and performance. Automated segmentation demonstrates significant clinical value by improving diagnostic precision, reducing missed diagnoses, streamlining treatment planning, and easing physician workload. Key challenges include lighting variability, vague lesion boundaries, multi-label complexities, and dataset limitations. Future directions, such as multi-modal learning, self-supervised techniques, spatio-temporal modeling, and rigorous clinical validation, are essential to enhance model robustness and ensure applicability in diverse healthcare settings for better patient outcomes.