Hypertensive Retinopathy (HR) is one of the most common retinal diseases. Hypertension diversely affects different body parts, including the eyes. Sustained hypertension can lead to damage in the retinal vasculature causing vision problems, this condition is termed as Hypertensive Retinopathy (HR). Since HR is a common condition associated with several Cardio-Vascular Diseases (CVD), automated diagnosis of HR can aid the physician when dealing with a large population. Classification of retinal vessels is the first step in characterizing retinal disorders such as HR. In this work, we have proposed an automated support system for classification of arteries and veins (AV) for HR detection. The proposed framework classifies AV using different feature vectors obtained through Radon vessel tracking algorithm. The features are extracted from publicly available standard dataset DRIVE. One of the main advantages of vessel tracking is that it can be further utilized for detecting Artery Vein Ratio (AVR). Classification results are acquired using three different classifiers namely SVM, NN and CNN. Experimental results show that CNN outperforms NN and SVM in AV classification for HR detection.
M. Kiruthika, Swapna, T. R., Santhosh, K. C., and Peeyush, K. P., “Artery and Vein classification for hypertensive retinopathy”, in 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, India, 2019.