Diabetic Retinopathy Deep Learning Model for Image Class Prediction by Modified Frog Leaping Optimized Features

Abstract

The healthcare sector greatly benefits from image classification, which holds immense value in medical diagnostics. One of the critical health concerns identified by the WHO is diabetic retinopathy (DR), a widespread condition that poses a serious threat to vision and has reached epidemic proportions worldwide.  This paper has proposed a DRDLOFLM (Diabetic Retinopathy Deep Learning Optimized Frog Leaping Model) model that predicts the class of the DR disease. For training of machine visual content features are extracted from the image. Selected features are extract from the blocked image so cumulative prediction gives better output of image diagnosis. Block region were selected by the frog leaping algorithm for the optimization of features. Experiment is perform on read medical images of DR images. Results shows that proposed DRDLOFLM (Diabetic Retinopathy Deep Learning Optimized Frog Leaping Model) has improved the evaluation parameters values.