Digestive tract bubble recognition model based on ViT

Yili Li; Haiji Zhang; Hanwen Zhang

doi:10.1117/12.3029763

21 June 2024 Digestive tract bubble recognition model based on ViT

Yili Li, Haiji Zhang, Hanwen Zhang

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131673P (2024) https://doi.org/10.1117/12.3029763
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

The clinical diagnosis of gastrointestinal diseases heavily relies on endoscopic examinations, utilizing medical electronic endoscopy systems that comprise various components. Gastrointestinal endoscopy categorizes regions, with upper and lower digestive tracts distinguished. The manual diagnosis process encounters challenge due to factors like variability, time constraints, and extensive image pools, leading to missed diagnoses. This paper proposes an automated bubble analysis and recognition method using Transformers, addressing image quality issues caused by motion blur, excessive brightness, and inadequate exposure. A Transformer-based neural network model is constructed to classify and diagnose bubble images. Contributions include a neural network model, targeted preprocessing techniques, and a performance evaluation with a substantial collection of gastrointestinal bubble images, demonstrating high accuracy in classification and diagnosis for both clear and blurry images. On clear images, the TOP1 accuracy can reach 0.932, and on blurry images, the TOP1 accuracy can reach 0.9375.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yili Li, Haiji Zhang, and Hanwen Zhang "Digestive tract bubble recognition model based on ViT", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131673P (21 June 2024); https://doi.org/10.1117/12.3029763

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