Classification of Eye Diseases Using Deep Learning

Past examinations of the eyes by ophthalmologists resulted in the identification of conditions like cataracts, glaucoma, and diabetic retinopathy. Special skills and substantial time commitment are required for implementing these assessment methods. The research proposes an advanced deep learning framework that integrates Vision Transformer (ViT) models alongside Convolutional Neural Networks (CNNs) for enhancing retinal image-based medical diagnosis operations. Local information that CNNs process in detail becomes stronger through the long-range associations that ViTs enable for better classification results. Employing the proposed model leads to successful early disease detection according to experimental findings because it achieves superior performance when compared to standard CNN approaches. The use of contemporary artificial intelligence approaches leads to improved ophthalmic treatments through better medical service accessibility for regions with restricted healthcare infrastructure.