Retinal Image Restoration and Vessel Segmentation using Modified Cycle-CBAM and CBAM-UNet. (arXiv:2209.04234v2 [eess.IV] UPDATED)

Clinical screening with low-quality fundus images is challenging and
significantly leads to misdiagnosis. This paper addresses the issue of
improving the retinal image quality and vessel segmentation through retinal
image restoration. More specifically, a cycle-consistent generative adversarial
network (CycleGAN) with a convolution block attention module (CBAM) is used for
retinal image restoration. A modified UNet is used for retinal vessel
segmentation for the restored retinal images (CBAM-UNet). The proposed model
consists of two generators and two discriminators. Generators translate images …

arxiv image segmentation unet