A Multi-Stage Detection of Diabetic Retinopathy in Fundus Images Using Convolutional Neural Network

Abstract

Diabetic Retinopathy (DRY) is a microvascular complication caused by diabetes mellitus, and it is one of the leading causes of blindness, especially in human adults. As the prevalence of this disease is growing exponentially, the screening of millions of people needs to be performed at a proliferating rate to diagnose the stage of the disease in its early stages. Highly advanced in the domain of technology, especially in artificial intelligence and its allied techniques, has come for the screening of DRY in photography to enhance the quality of life. This generates a bulk size of data that travels at high speed and cuts down on many human tasks. However, the techniques employed by the authors so far are quite expensive and time-consuming, and the prediction rate is insufficient to apply in a real-time scenario. This study offered a road for a deep learning-based fully automated system that helps to save manual disease diagnosis work and achieve disease detection in its very early stage using EfficientNetB3 (ENB3) Convolutional Neural Network (CNN) on DRY Fundus Images (FDI). In the suggested CNN, architectural variations and pre-processing techniques such as dimensionality reduction, global average pooling, and circular cropping are introduced alongside the Leaky ReLU (LR) activation function, Transfer Learning, and Reduce LROnPlateau technique, respectively. The accuracy of the proposed CNN classifier was 94.2% on training data, with a kappa score of 0.874, while it achieved a high level of accuracy at 96.7% on the testing data for DRY grading. Further, the evaluation results presented that the proposed model efficiently classifies the DRY stages for early disease detection.