A hybrid model for early diagnosis of ophthalmology diseases leveraging CNNs, SBOA optimization, and XAI for visualization

Abstract

Ophthalmology diseases are among the leading causes of vision loss worldwide. Glaucoma, diabetic retinopathy, and cataracts are the most common diseases and can lead to permanent vision loss if left untreated. In this paper, a new hybrid model has been proposed with the methods accepted in the literature used in the early diagnosis of these diseases. The relationships between imaging analyses and clinical evaluations performed in the diagnostic processes of glaucoma, diabetic retinopathy, and cataract are discussed, and the methods that help to identify diseases in the early stages are emphasized. In addition, the contributions of advanced technologies and imaging systems used in diagnosing these diseases to the developments in the field of eye health are discussed. This article proposes a hybrid model for eye disease detection that combines Convolutional Neural Networks (CNNs) with a metaheuristic optimization algorithm. This model uses ShuffleNet and ResNet101 models as feature extractors, while the Secretary Bird Optimization Algorithm (SBOA) is used for feature selection. Then, the extracted feature maps were combined with ShuffleNet and ResNet101 and optimized with SBOA. The feature fusion process aimed to improve the performance of the developed model by combining different features of the same image. The combined feature map optimized with SBOA was classified into six different classifiers so that the model could work faster and more effectively. Competitive results were produced in the developed model. Finally, explainable artificial intelligence methods were used to visualize the decisions of the developed hybrid model and understand the internal working principle of the model.