Comparative analysis of vision transformers and conventional convolutional neural networks in detecting referable diabetic retinopathy

Comparative analysis of vision transformers and conventional convolutional neural networks in detecting referable diabetic retinopathy

Objective: Vision transformers (ViTs) have shown promising performance in various classification tasks previously dominated by convolutional neural networks (CNNs). However, the performance of ViTs in referable diabetic retinopathy (DR) detection is relatively underexplored. In this study, using ret...

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Bibliographic Details
Main Authors: Goh, Jocelyn Hui Lin, Ang, Elroy, Srinivasan, Sahana, Lei, Xiaofeng, Loh, Johnathan, Quek, Ten Cheer, Xue, Cancan, Xu, Xinxing, Liu, Yong, Cheng, Ching-Yu, Rajapakse, Jagath Chandana, Tham, Yih-Chung
Other Authors: School of Computer Science and Engineering
Format: Journal Article
Language:English
Published: 2024
Subjects:
Computer and Information Science
Convolutional neural network
Referable diabetic retinopathy
Online Access:https://hdl.handle.net/10356/180451

Internet

https://hdl.handle.net/10356/180451

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