Vision Transformer Model for Gastrointestinal Tract Diseases Classification from WCE Images

Accurate disease classification utilizing endoscopic images indeed poses a significant challenge within the field of gastroenterology. This research introduces a methodology for assisting medical diagnostic procedures and detecting gastrointestinal (GI) tract diseases by categorizing features extracted from endoscopic images using Vision Transformer (ViT) models. We propose three ViT-inspired models for classifying GI tract diseases using colon images acquired through wireless capsule endoscopy (WCE). The highest achieved accuracy among our models is 97.83%. We conducted a comparative analysis with three pre-trained CNN (Convolutional Neural Network) models namely, Xception, DenseNet121, and MobileNet, alongside recent research papers to validate our findings.