Develop an Ensemble Transfer Learning with Vision Transformers Large Model for Diabetic Retinopathy detection at an earlier stage

Proper treatment for Diabetic Retinopathy (DR), a leading cause of vision loss globally, depends on quick and accurate diagnosis. Existing diagnostic techniques are often labour-intensive and rely on manual analysis which can lead to inconsistent results. To enhance the timely detection of DR from retinal fundus images, this research proposes a novel method that integrates Vision Transformers (ViT) Large Model with advanced ensemble transfer learning methods such as VGG19, Adaptive Lightweight DenseNet, ResNet with Dynamic Depth, XceptionNet for Multi-Task Learning, and MobileNetV3 with Attention Mechanisms. The primary challenges addressed include the scarcity of labelled datasets, the difficulty in capturing both local and global image features, and the need for cost-effective methods suitable for large-scale screenings. The proposed approach combines pre-trained machine learning models with Sparse Attention Mechanisms to focus on disease-relevant regions in the images, while vision transformers capture the global context. The objective is to achieve superior diagnostic performance in terms of precision, sensitivity, and accuracy, while ensuring adaptability and robustness for clinical applications. Experimental results demonstrate that the ensemble methodology is highly effective in the early detection of DR significantly outperforming conventional deep learning models. Proposed system offers pathway for rapid, accurate, and automated diagnosis enabling timely intervention and improved patient outcomes.