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Generative Adversarial Network Combined with SE-ResNet and Dilated Inception Block for Segmenting Retinal Vessels
2022
Computational Intelligence and Neuroscience
This study develops an accurate method based on the generative adversarial network (GAN) that targets the issue of the current discontinuity of micro vessel segmentation in the retinal segmentation images. The processing of images has become increasingly efficient since the advent of deep learning method. We have proposed an improved GAN combined with SE-ResNet and dilated inception block for the segmenting retinal vessels (SAD-GAN). The GAN model has been improved with respect to the following
doi:10.1155/2022/3585506
pmid:36072751
pmcid:PMC9441346
fatcat:cxhlkiz43zbath5jkifjlpdfrq