High-Speed and Accurate Diagnosis of Gastrointestinal Disease: Learning on Endoscopy Images Using Lightweight Transformer with Local Feature Attention

High-Speed and Accurate Diagnosis of Gastrointestinal Disease: Learning on Endoscopy Images Using Lightweight Transformer with Local Feature Attention

In response to the pressing need for robust disease diagnosis from gastrointestinal tract (GIT) endoscopic images, we proposed FLATer, a fast, lightweight, and highly accurate transformer-based model. FLATer consists of a residual block, a vision transformer module, and a spatial attention block, wh...

Full description

Bibliographic Details
Main Authors: Shibin Wu, Ruxin Zhang, Jiayi Yan, Chengquan Li, Qicai Liu, Liyang Wang, Haoqian Wang
Format: Article
Language:English
Published: MDPI AG 2023-12-01
Series:Bioengineering
Subjects:
gastrointestinal disease
lightweight
fast
accurate
transformer-based model
Online Access:https://www.mdpi.com/2306-5354/10/12/1416

Internet

https://www.mdpi.com/2306-5354/10/12/1416

Similar Items