Blood Vessel Detection in the Retina Using Convolution Neural Network

Modern-era developments in authentication systems have changed from traditional methods based on passwords or signatures to new methods based on biometric patterns. Biometric patterns are unique to each person, and identifying individuals has become much more accurate. Biometric cognition uses an intelligent method to identify a person with some unique characteristics of a human being. Unlike traditional methods, these biometric methods are more reliable and safer. Diagnosing blood patterns of retinal images is one of the safest ways to authenticate taking into consideration the monopoly nature of these patterns for each individual and their non-reproducibility and alteration. In the present study, the Convolutional Neural Network (CNN) was used to identify the pattern of blood vessels in the retina. DRIVE dataset was used to evaluate results. The images of the retina of different people were stored in this dataset. After extracting the patterns within the retinal layers for each person as a model indicating the identity of these individuals, the patterns related to the training and testing datasets were compared to determine the identity of individuals.  Properly tested samples increase the accuracy of the proposed method, while incorrect detection will cause an error in the proposed method. The results showed that the average accuracy of matching blood vessel patterns for retinal images in the proposed method was 94.83%, which is high and comparable to previous methods.