Digestive tract bubble recognition model based on ViT

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.