# Gastrointestinal Disorder Detection with a Transformer Based Approach

arXiv: https://arxiv.org/abs/2210.03168 · HTML: https://mlsanity.com/paper/2210.03168/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Scaling Vision Transformers](https://mlsanity.com/paper/2106.04560/) (2021)
- [Vision Transformers with Patch Diversification](https://mlsanity.com/paper/2104.12753/) (2021)

## Built on by (most cited first, 3 of 3)

- [Vision Transformer for Efficient Chest X-ray and Gastrointestinal Image Classification](https://mlsanity.com/paper/2304.11529/) (2023)
- [Improving Segmentation of Breast Ultrasound Images: Semi Automatic Two Pointers Histogram Splitting Technique](https://mlsanity.com/paper/2210.10975/) (2022)
- [Endoscopy Classification Model Using Swin Transformer and Saliency Map](https://mlsanity.com/paper/2303.06736/) (2023)

## Abstract (arXiv)

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.