SwinECAT combines Swin Transformer with ECA channel attention and reports 88.29% accuracy on 9-class fundus disease classification on EDID, though the claim rests on a single data split.
Automated identification and grading system of diabetic retinopathy using deep neural networks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention
SwinECAT combines Swin Transformer with ECA channel attention and reports 88.29% accuracy on 9-class fundus disease classification on EDID, though the claim rests on a single data split.