A Deep Learning Grading Classification of Diabetic Retinopathy on Retinal Fundus Images with Bio-inspired Optimization

Abstract

Diabetic Retinopathy (DR) is considered the major cause of impaired vision for diabetic patients, particularly in developing counties. Treatment includes maintaining the patient’s present grade of vision as the illness can be irreparable. Initial recognition of DR is highly important to effectively sustain the vision of the patients. The main problem in DR recognition is that the manual diagnosis procedure consumes time, effort, and money and also includes an ophthalmologist’s analysis of retinal fundus imaging. Machine Learning (ML)-related medical image analysis is proven to be capable of evaluating retinal fundus images, and by using Deep Learning (DL) techniques. The current research presents an Automated DR detection method by utilizing the Glowworm Swarm Optimization (GSO) with Deep Learning (ADR-GSODL) approach on retinal fundus images. The main aim of the ADR-GSODL technique relies on the recognizing and classifying process of DR in retinal fundus images. To obtain this, the introduced ADR-GSODL method enforces Median Filtering (MF) as a pre-processing step. Besides, the ADR-GSODL technique utilizes the NASNetLarge method for deriving the GSO, and feature vectors are applied for parameter tuning. For the DR classification process, the Variational Autoencoder (VAE) technique is exploited. The supremacy of the ADR-GSODL approach was confirmed by a comparative simulation study.