# Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading

arXiv: https://arxiv.org/abs/2010.15344 · HTML: https://mlsanity.com/paper/2010.15344/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [Two Eyes Are Better Than One: Exploiting Binocular Correlation for Diabetic Retinopathy Severity Grading](https://mlsanity.com/paper/2108.06763/) (2021)
- [Time Majority Voting, a PC-based EEG Classifier for Non-expert Users](https://mlsanity.com/paper/2207.12662/) (2022)
- [Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researchers](https://mlsanity.com/paper/2307.02819/) (2023)
- [Object-Aware Self-supervised Multi-Label Learning](https://mlsanity.com/paper/2205.07028/) (2022)
- [Residual Channel Attention Network for Brain Glioma Segmentation](https://mlsanity.com/paper/2205.10758/) (2022)
- [EEG4Students: An Experimental Design for EEG Data Collection and Machine Learning Analysis](https://mlsanity.com/paper/2208.11743/) (2022)

## Abstract (arXiv)

Diabetes is one of the most common disease in individuals. textitDiabetic 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.