A novel method for stroke prediction from retinal images using HoG approach

Abstract

Stroke is one of the principal reasons for adult impairment worldwide. Retinal fundus images are analyzed for the detection of various cardiovascular diseases like Stroke. Stroke is mainly characterized by soft and hard exudates, artery or vein occlusion and alterations in retinal vasculature. In this research work, Histogram of Oriented Gradients (HoG) has been implemented to extract features from the region of interest of retinal fundus images. This innovative method is assessed for the computer aided diagnosis of normal healthy and abnormal images of stroke patients. A comparative analysis has been made between the extracted HoG features and Haralick features. HoG features extracted from the region of interest, when given to a Naïve Bayes classifier provides an accuracy of 93% and a Receiver Operating Characteristic (ROC) curve area of 0.979.