Multi-class Classification of Retinal Eye Diseases from Ophthalmoscopy Images Using Transfer Learning-Based Vision Transformers

Abstract

This study explores a transfer learning approach with vision transformers (ViTs) and convolutional neural networks (CNNs) for classifying retinal diseases, specifically diabetic retinopathy, glaucoma, and cataracts, from ophthalmoscopy images. Using a balanced subset of 4217 images and ophthalmology-specific pretrained ViT backbones, this method demonstrates significant improvements in classification accuracy, offering potential for broader applications in medical imaging. Glaucoma, diabetic retinopathy, and cataracts are common eye diseases that can cause vision loss if not treated. These diseases must be identified in the early stages to prevent eye damage progression. This paper focuses on the accurate identification and analysis of disparate eye diseases, including glaucoma, diabetic retinopathy, and cataracts, using ophthalmoscopy images. Deep learning (DL) has been widely used in image recognition for the early detection and treatment of eye diseases. In this study, ResNet50, DenseNet121, Inception-ResNetV2, and six variations of ViT are employed, and their performance in diagnosing diseases such as glaucoma, cataracts, and diabetic retinopathy is evaluated. In particular, the article uses the vision transformer model as an automated method to diagnose retinal eye diseases, highlighting the accuracy of pre-trained deep transfer learning (DTL) structures. The updated ViT#5 model with the augmented-regularized pre-trained model (AugReg ViT-L/16_224) and learning rate of 0.00002 outperforms the state-of-the-art techniques, obtaining a data-based accuracy score of 98.1% on a publicly accessible retinal ophthalmoscopy image dataset, which includes 4217 images. In most categories, the model outperforms other convolutional-based and ViT models in terms of accuracy, precision, recall, and F1 score. This research contributes significantly to medical image analysis, demonstrating the potential of AI in enhancing the precision of eye disease diagnoses and advocating for the integration of artificial intelligence in medical diagnostics.