Gastrointestinal Disorder Detection with a Transformer Based Approach
This work addresses disease categorization in gastroenterology, offering a potential tool for medical diagnosis, but it is incremental as it applies an existing method to a specific domain.
The paper tackled gastrointestinal disorder detection from endoscopic images using a vision transformer, achieving 95.63% accuracy and outperforming DenseNet201 in various metrics.
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.