RETRACTED ARTICLE: Robust retinal blood vessel segmentation using convolutional neural network and support vector machine

Abstract

In recent decades, automatic retinal blood vessel segmentation and classification (RBVSC) helps to determine many diseases such as glaucoma, hypertension, macular-degeneration, diabetes-mellitus, etc. The early recognition of these disorders is essential for preventing patients from blindness. In this work, a new supervised system was developed to enhance the performance of RBVSC. At first, the input retinal images were collected from two datasets such as: Digital Retinal Image for Vessel Extraction (DRIVE) and STARE (STructured Analysis of the Retina). Then, the retinal vessels were segmented utilizing mean orientation based super-pixel segmentation. Besides, Convolutional Neural Network (CNN) was applied to extract the feature vectors from segmented regions. Finally, a binary classifier [Support Vector Machine (SVM)] performs classification on the extracted features for classifying the “vessel” and “non-vessel” regions. The combination of CNN and SVM automatically learns the feature values from raw images and classifies the patterns easily. From the experimental study, the proposed system improved RBVSC up to 2–4% compared to other existing systems and classification methodologies: Deep Neural Network (DNN), Random Forest (RF) and Naive Bayes (NB) by means of specificity, accuracy, sensitivity and kappa index.