Syed Irtaza Haider1, Khursheed Aurangzeb2,*, Musaed Alhussein2
CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1501-1526, 2022, DOI:10.32604/cmc.2022.025479
Abstract The accurate segmentation of retinal vessels is a challenging task due to the presence of various pathologies as well as the low-contrast of thin vessels and non-uniform illumination. In recent years, encoder-decoder networks have achieved outstanding performance in retinal vessel segmentation at the cost of high computational complexity. To address the aforementioned challenges and to reduce the computational complexity, we propose a lightweight convolutional neural network (CNN)-based encoder-decoder deep learning model for accurate retinal vessels segmentation. The proposed deep learning model consists of encoder-decoder architecture along with bottleneck layers that consist of depth-wise squeezing, followed by full-convolution, and finally depth-wise… More >
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