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- W4304192533 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." @default.
- W4304192533 created "2022-10-11" @default.
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- W4304192533 date "2022-10-06" @default.
- W4304192533 modified "2023-09-27" @default.
- W4304192533 title "Gastrointestinal Disorder Detection with a Transformer Based Approach" @default.
- W4304192533 doi "https://doi.org/10.48550/arxiv.2210.03168" @default.
- W4304192533 hasPublicationYear "2022" @default.
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