Application Research of Improved U-shaped Network in Detection of Retinopathy

Abstract

Fundus retinal blood vessel analysis and detection of exudates and bleeding points are important methods for judging the degree of diabetic retinopathy. Aiming at the problems such as poor segmentation effect of bifurcation and end points of microvessels, unclear exudate boundary, difficult segmentation of small and scattered bleeding points, an improved U-shaped network is proposed to extract more rich high-level features by improving the context extraction coding module. And in the feature encoding stage, a hybrid attention mechanism (HAM) is added to highlight the features of microvessels and lesions, and reduce the impact of background and noise. Experimental results show that the segmentation accuracy, sensitivity, specificity and AUC value of the proposed algorithm on the fundus retinal blood vessel segmentation dataset DRIVE are better than U-NET, CE-NET and other existing methods. The sensitivity is increased by 0.0146 compared with CE-Net network. On diabetic retinopathy lesion segmentation dataset DIARETDB1, the segmentation effect of exudates and bleeding points is better than U-NET, CE-NET and other existing methods, which can effectively assist doctors in diagnosis.

Keywords

• |fundus retinal blood vessel|exudate|bleeding point|context feature encoder|attention mechanism