Accurate disease categorization using endoscopic images is a significant
problem in Gastroenterology. This paper describes a technique for assisting
medical diagnosis procedures and identifying gastrointestinal tract disorders
based on the categorization of characteristics taken from endoscopic pictures
using a vision transformer and transfer learning model. Vision transformer has
shown very promising results on difficult image classification tasks. In this
paper, we have suggested a vision transformer based approach to detect
gastrointestianl diseases from wireless capsule endoscopy (WCE) curated images
of colon with an accuracy of 95.63\%. We have compared this transformer based
approach with pretrained convolutional neural network (CNN) model DenseNet201
and demonstrated that vision transformer surpassed DenseNet201 in various
quantitative performance evaluation metrics.

本研究使用视觉转换器和迁移学习模型对内窥镜图像进行特征分类，从而协助医疗诊断，识别肠道疾病，准确率可达 95.63％。与预训练的卷积神经网络模型 DenseNet201 进行比较，并证明视觉转换器在各种数量化性能评估指标上均优于 DenseNet201。