Cross-Validation Convolution Neural Network-Based Algorithm for Automated Detection of Diabetic Retinopathy

Abstract

The substantial vision loss due to Diabetic Retinopathy (DR) mainly damages the blood vessels of the retina. These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage, if this problem doesn’t exhibit initially, that leads to permanent blindness. So, this type of disorder can be only screened and identified through the processing of fundus images. The different stages in DR are Micro aneurysms (Ma), Hemorrhages (HE), and Exudates, and the stages in lesion show the chance of DR. For the advancement of early detection of DR in the eye we have developed the CNN-based identification approach on the fundus blood lesion image. The CNN-based automated detection of DR proposes the novel Graph cutter-built background and foreground superpixel segmentation technique and the foremost classification of fundus images feature was done through hybrid classifiers as K-Nearest Neighbor (KNN) classifier, Support Vector Machine (SVM) classifier, and Cascaded Rotation Forest (CRF) classifier. Over this classifier, the feature cross-validation made the classification more accurate and the comparison is made with the previous works of parameters such as specificity, sensitivity, and accuracy shows that the hybrid classifier attains excellent performance and achieves an overall accuracy of 98%. Among these Cascaded Rotation Forest (CRF) classifier has more accuracy than others.