Classification of Ocular Diseases Related to Diabetes Using Transfer Learning

Abstract

Although artificial intelligence enables the detection of abnormalities in medical images and is widely used as a computer vision technology, many researchers have focused on the detection of only one disease related to diabetes, which is diabetic retinopathy. In fact, patients face a significant risk of two other illnesses: cataract and glaucoma. In this article, we examined the diagnosis of these three eye diseases caused by diabetes and compared four approaches to classify these conditions. The proposed approaches are based on the transfer learning technique. We started by filtering, preparing, and augmenting the dataset, then applied transfer learning for feature extraction using two different architectures: VGG16 and RESNET50. We also investigated the impact of using contrast limited adaptive histogram equalization on the accuracy and precision of the models. This filter was used in a pre-training step for diabetic retinopathy diagnosis and in this paper proved its efficiency for glaucoma and cataract too. The final layers were replaced by Random Forest for classification. Models performed acceptable accuracies of 89.17% and 85.64% without operating contrast-limited adaptive histogram equalization and achieved better results when applying contrast-limited adaptive histogram equalization, with an accuracy of 97.48% and 96.66% for VGG16 and RESNET 50, respectively.