Deep Learning‐Based Classification of Diabetic Retinopathy and Macular Edema Diseases

ABSTRACT Diabetic retinopathy (DR) and diabetic macular edema (DME) are eye‐related diseases most commonly seen in persons with diabetes. Existing models did not achieve the desired level of accuracy or success in distinguishing DR and DME. Thus, by resolving this issue, the performance and accuracy of automated classification systems are significantly improved. Therefore, a transfer learning model is developed to simultaneously classify DR and DME grades in retinal images. Initially, the input retinal fundus images are collected from standard databases. These images are given to the proposed Transfer Learning‐based Adaptive Fused Convolutional Neural Networks (TL‐AFCNN). This model fusion is created by using a Residual Attention Network (RAN) with multidilated DenseNet techniques. In order to improve the effectiveness of simultaneous classification using TL‐ACNN, the weights from this network are fine‐tuned via the Fitness Oriented Lotus Effect Optimization Algorithm (FO‐LEA). From, the developed TL‐ACNN model, simultaneous grade classification outcomes of both DR and DME are attained. Therefore, the risk of vision loss is treated effectively by analyzing the grade‐classified outcomes from the proposed model. Finally, the simultaneous classification results attained by the suggested model are compared with existing models to verify its potential.