Deep learning system for assessing diabetic retinopathy prevalence and risk level estimation

Abstract

Diabetic retinopathy, one of the foremost problems brought on by Diabetes Mellitus has seen an exponential rise in incidence due to the exponential growth of diabetics worldwide and causes visual issues and sightlessness owing to deformity of individual retina. An early detection and diagnosis are necessary to stop DR from progressing into severe stages and to stop blindness for which regular screening of eye is mandatory. To do this, several machine learning (ML) models are available. However, when used with bigger datasets, classical ML models either need more training time and have less generalisation in feature extraction and classification than when used with smaller data volumes. As a result, Deep Learning (DL), a newer ML paradigm that can manage a relatively small data volume with aid of effective data processing methods is presented. They do, still, often use bigger data in the deep network structure to improve feature extraction and picture classification performance. This study presents a CNN model for DR classification and compares with other variants of pre-trained DL models for initial recognition of DR through binary and multi-class classification. The attained result of 97% accuracy reveals that pre-trained ResNet model’s efficacy is better in diagnosing DR.