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Assessing deep learning models for multi-class upper endoscopic disease segmentation: A comprehensive comparative

In Neng Chan¹, Pak Kin Wong², Tao Yan³

¹Department of Electromechanical Engineering, University of Macau, Macau 999078, China.

World Journal of Gastroenterology

|November 20, 2025

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This summary is machine-generated.

Deep learning models show promise for segmenting upper gastrointestinal diseases, with hierarchical architectures like Swin-UMamba and SegFormer demonstrating high accuracy. Further clinical validation is essential for real-world application in endoscopy.

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Upper gastrointestinal (UGI) diseases pose diagnostic challenges in endoscopy due to visual similarities and observer variability.
Automated segmentation using deep learning (DL) can aid endoscopists, but multi-class UGI disease segmentation is underexplored.
Clinical validation of DL models for UGI disease segmentation is limited, hindering real-world application.

Purpose of the Study:

To evaluate 17 state-of-the-art deep learning (DL) models for multi-class upper gastrointestinal (UGI) disease segmentation.
To assess the clinical translation and real-world applicability of different DL architectures for UGI endoscopy.
To identify DL models that can reduce diagnostic errors and support clinical decision-making.

Main Methods:

Keywords:

Deep learning Disease segmentation Gastrointestinal diseases Medical imaging Upper endoscopy

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Evaluated 17 DL models (CNN, transformer, mamba-based) on a self-collected dataset (3313 images, 9 classes) and the EDD2020 dataset (386 images, 5 classes).
Assessed segmentation performance (IoU) and the performance-efficiency trade-off for each model.
Conducted statistical analyses and cross-dataset evaluations to measure performance differences and generalization capabilities.

Main Results:

Swin-UMamba achieved the highest segmentation performance (IoU: 89.06% self-collected, 77.53% EDD2020), followed by SegFormer and ConvNeXt + UPerNet.
SegFormer offered the best accuracy-efficiency balance (92.02%), suitable for real-time clinical use.
Transformer-based models generalized better (64.78%-71.52% retention) in cross-dataset evaluations, despite overall performance drops.

Conclusions:

Hierarchical DL architectures like Swin-UMamba and SegFormer show significant potential for UGI disease segmentation.
These models can help reduce missed diagnoses and improve endoscopic workflow efficiency.
Robust clinical validation is critical before widespread real-world deployment in endoscopic practice.