Abstract
ABSTRACT Continuous monitoring of artery and vein vessels in the human eye to prevent the loss of eyesight is essential in the diagnosis of diabetic retinopathy. The progression of diabetic retinopathy is echoed by the internal anatomical changes of the retina, such as changes in the artery vein ratio, formation of fake vessels, tortuosity, lesion formation, etc. Among these symptoms, calculation of the artery vein ratio is still a challenging task since the visibility of the artery and vein changes over different regions in the fundus image. The proposed Atrous Depth Concatenated neural network with the enriched encoder (EEDCFCNN) architecture for artery vein classification is based on the deep semantic segmentation architecture. The proposed architecture can achieve an improved result on the public databases DRIVE, INSPIRE, and IOSTAR.
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