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

TL;DR

Sea-Net is a novel deep learning architecture that combines spatial and channel attention mechanisms with a hybrid loss function to improve diabetic retinopathy grading from retinal images.

Contribution

The paper introduces SEA-Net, a new architecture that enhances DR grading accuracy by integrating attention modules and a hybrid loss function, addressing subtle feature differences.

Findings

Improved classification accuracy on DR datasets.

Effective attention mechanism enhances feature extraction.

Hybrid loss reduces intra-class variability.

Abstract

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.