# Gastrointestinal Disorder Detection with a Transformer Based Approach Source: [Gastrointestinal Disorder Detection with a Transformer Based Approach]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:51:11.369Z Authors: A.K.M. Salman Hosain, Mynul islam, Md Humaion Kabir Mehedi, Irteza Enan Kabir, Zarin Tasnim Khan Published: 2022-10-06T19:08:37.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()