Retinal Blood Vessel Segmentation and Analysis using Lightweight Spatial Attention based CNN and Data Augmentation

The exact detection and creation of treatment regimens for conditions like diabetic retinopathy and hypertensive retinopathy depend on the segmentation of retinal blood vessels. Methods based on deep learning have been employed in the last ten years to segment blood vessels in fundus images. Due to the lack of uniform data in large quantities, the wide range of brightness and anatomical structures of the fundus images that are available, and the variety of shapes and sizes of the vessels in the tree-like vascular structure, it is still difficult to accurately segment all the vessels in a retinal fundus image. In this study, we present a unique lightweight CNN with an encoder-decoder structure for real-time and precise segmentation of blood vessels. The most popular retina datasets, DRIVE and CHASE, were used to train and evaluate the model. With an accuracy of 96.3% and 78.45% f1 score with respect to the DRIVE dataset and accuracy of 97.14% and 82.79% f1 score with respect to the CHASE dataset, we can observe that the model is lightweight and has provided comparable performance. Additionally, the suggested model runs faster with an average inference time of 0.0059 seconds and has fewer parameters than state-of-the-art models currently in use.