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This study investigates the use of transfer learning with vision transformers (ViTs) and convolutional neural networks (CNNs) for the classification of retinal diseases such as diabetic retinopathy, glaucoma, and cataracts from ophthalmoscopy images. Utilizing a balanced dataset of 4217 images, the research demonstrates that the updated ViT#5 model achieves a remarkable accuracy of 98.1%, outperforming existing techniques in various performance metrics. The findings highlight the potential of AI in enhancin

Author

alinohririzwan196541

Language

EN