An Attention Mechanism-based U-Net Fundus Image Segmentation Algorithm

doi:10.3969/j.issn.1006-2475.2024.05.019

Abstract

Abstract: Abstract： The radius and width of retinal fundus vessels are important indicators for assessing eye diseases， so accurate segmentation of fundus images is becoming increasingly meaningful. In order to effectively assist doctors in diagnosing eye diseases， the paper proposes a new neural network to segment fundus vascular images. The basic idea is to reduce the information loss by improving the traditional U-Net model with the help of an attention fusion mechanism， using Transformer to construct a channel attention mechanism and a spatial attention mechanism， and fusing the information obtained by the two attention mechanisms. In addition， the number of retinal fundus images is relatively small， and the coefficients of the neural network are relatively large， which are prone to overfitting during training， so the DropBlock layer is introduced to solve this problem. The proposed method was validated on the publicly available dataset DRIVE and compared with several state-of-the-art methods. The results show that our method achieved the highest ACC value of 0.967 and the highest F1 value of 0.787. These experimental results demonstrate that the proposed method is effective in segmenting retinal fundus images.

Key words: Key words： retinal fundus image segmentation, attention mechanism, DropOut layer

CLC Number:

TP391.4

ZHANG Zixu, LI Jiaying, LUAN Pengpeng, PENG Yuanyuan. An Attention Mechanism-based U-Net Fundus Image Segmentation Algorithm[J]. Computer and Modernization, 2024, 0(05): 110-114.

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