A Computer Aided Diagnostic System of Hypertensive Retinopathy Using Deep Learning

Abstract

Hypertension-related Retinopathy (HR) is the medical term for an illness that can have an effect on the retina. The severity and length of time that a person has had hyperten sion is strongly correlated with the prevalence of HR-eye disease. The following are the ocular pathological abnormalities that are related with HR: constriction of the arterio lar blood vessels, macular edoema, retinal haemorrhage, cotton wool patches, and blood vessels. In order to forestall the development of blindness, it is absolutely necessary to detect and evaluate HR as quickly as humanly possible. The number of computer-aided diagnostic systems (CADx) currently available on the market that are able to diagnose HR is extremely limited. In addition to having in-depth expertise in the field, being able to operate these CADx systems needed sophisticated knowledge of the various im age processing techniques. In recent years, it has been discovered that categorization algorithms have an unsatisfactory level of accuracy. To overcome these challenges, a brand new CAD-HR system that consists of a depthwise separable convolutional neural network (DSC) with residual connection and a linear support vector machine (LSVM) has been constructed. This system was developed in order to meet the aforementioned challenges. In order to increase the total size of the datasets, the data augmentation technique is first used to retina graphics. This is done with the intention of improving the quality of the data. After that, the DSC method is used on photographs of the retina in order to obtain consistent properties. In the final step, an LSVM classifier is utilised to make the determination of whether or not the retinal samples represent HR or non-HR. The statistical investigation of 9,500 renography images obtained from two sources that are available to the public and one source that is kept private in order to evaluate the accuracy of the implementation of the CAD-HR system utilising several performance metrics including accuracy, specificity, and the AUC-ROC curve. These performance metrics were used in order to evaluate the proficiency of the implementation of the CAD-HR system. It has been established that the DSC-HR model takes less computing time and fewer parameters in order to accurately categorise HR phases.