| Autor: |
Hosain, A. K. M. Salman, islam, Mynul, Mehedi, Md Humaion Kabir, Kabir, Irteza Enan, Khan, Zarin Tasnim |
| Rok vydání: |
2022 |
| Předmět: |
|
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/IEMCON56893.2022.9946531 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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