School of Computer, Henan University of Engineering, Zhengzhou 451191, China.
School of Electrical Information Engineering, Henan University of Engineering, Zhengzhou 451191, China.
Comput Math Methods Med. 2022 Jul 6;2022:8111883. doi: 10.1155/2022/8111883. eCollection 2022.
Aiming at the problem of insufficient details of retinal blood vessel segmentation in current research methods, this paper proposes a multiscale feature fusion residual network based on dual attention. Specifically, a feature fusion residual module with adaptive calibration weight features is designed, which avoids gradient dispersion and network degradation while effectively extracting image details. The SA module and ECA module are used many times in the backbone feature extraction network to adaptively select the focus position to generate more discriminative feature representations; at the same time, the information of different levels of the network is fused, and long-range and short-range features are used. This method aggregates low-level and high-level feature information, which effectively improves the segmentation performance. The experimental results show that the method in this paper achieves the classification accuracy of 0.9795 and 0.9785 on the STARE and DRIVE datasets, respectively, and the classification performance is better than the current mainstream methods.
针对当前研究方法中视网膜血管分割细节不足的问题，本文提出了一种基于双注意力的多尺度特征融合残差网络。具体来说，设计了一个具有自适应校准权重特征的特征融合残差模块，在有效提取图像细节的同时避免了梯度弥散和网络退化。在骨干特征提取网络中多次使用 SA 模块和 ECA 模块自适应选择焦点位置，生成更具判别性的特征表示；同时融合网络不同层次的信息，利用长短程特征。该方法聚合了低层次和高层次的特征信息，有效提高了分割性能。实验结果表明，本文方法在 STARE 和 DRIVE 数据集上的分类准确率分别达到 0.9795 和 0.9785，分类性能优于当前主流方法。
