Automatic Classification of Hypertensive Retinopathy by Gray Wolf Optimization Algorithm and Naive Bayes Classification

Retinal blood vessels are affected by a variety of eye diseases, including hypertensive retinopathy (HR) and diabetic retinopathy (DR). A person with HR needs to be sure to check their eyes regularly, which requires the use of computer vision methods to analyze images of the back of the eye and help ophthalmologists automatically. Automated diagnostic systems are useful for diagnosing different retinal diseases for ophthalmologists and patients who need to establish an automated HR detection and classification system using retinal images. In this work, a sliding band filter was used to improve the back-of-the-eye images and small convex regions to develop an automated system for detecting and classifying HR gravity levels. An image classification with improved wolf optimization along Bayes algorithm was conducted. The current model was tested using the publicly available dataset, and its results were compared to existing models. The results mentioned that the model-improved Naive Bayes model classified the different HR severity levels on the optimized features and produced a maximum accuracy of 100% while being compared to other classifiers.