Diabetic retinopathy detection method using artificial neural network

Abstract

Diabetic retinopathy (DR) is one of the leading causes of vision loss in diabetics worldwide. DR is a microvascular disease that affects the eye's retina, leading to vascular blockage and thus disrupting the primary source of nourishment for the retinal tissue. This paper merged a considerable range of known classification algorithms into a sophisticated diagnostic model and a new multilayer perceptron (MLP)-based classifier. The latter achieved the highest accuracy rates among all other classifiers, including the previously introduced ensemble model. Four sub-datasets were generated by WrapperSubsetEval. and InfoGainEval. Feature selection algorithms achieved 75.8% and 70.7% accuracy rates on the best performing sub dataset (InfoGainEval. top 5) by the ensemble model and the new MLP-based classifier. The results express the splendid performance of the partial dataset with the MLP artificial neural network, which contributes significantly to a less complex classification process than the original full Messidor dataset.