Diabetes is one of the most common disease in individuals. \textit{Diabetic retinopathy} (DR) is a complication of diabetes, which could lead to blindness. Automatic DR grading based on retinal images provides a great diagnostic and prognostic value for treatment planning. However, the subtle differences among severity levels make it difficult to capture important features using conventional methods. To alleviate the problems, a new deep learning architecture for robust DR grading is proposed, referred to as SEA-Net, in which, spatial attention and channel attention are alternatively carried out and boosted with each other, improving the classification performance. In addition, a hybrid loss function is proposed to further maximize the inter-class distance and reduce the intra-class variability. Experimental results have shown the effectiveness of the proposed architecture.
@article{arxiv.2010.15344,
title = {Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading},
author = {Ziyuan Zhao and Kartik Chopra and Zeng Zeng and Xiaoli Li},
journal= {arXiv preprint arXiv:2010.15344},
year = {2020}
}