Predicting Diabetic Retinopathy Using Deep Learning

Abstract

AI is a crucial tool in early detection and classification of diabetic retinopathy, which is a leading cause of visual impairment, has opened up new avenues for further research and development. Transfer Learning (TL) has improved the accuracy of predictions and classifications within training datasets, surpassing existing methodologies. This study provides comprehensive insights into current databases, screening programs, performance evaluation metrics, relevant biomarkers, and challenges encountered in ophthalmology. The findings underscore the potential of AI-based approaches in enhancing diagnostic precision and offer a promising direction for future studies. It delineates further research and development opportunities in integrating AI advancements in the field. The findings underscore the efficacy of Transfer Learning in significantly improving the accuracy of diabetic retinopathy image predictions. This newlineresearch highlights the potential of AI-based approaches in enhancing diagnostic precision and offers a promising direction for future studies. Among prevalent medical complications, Diabetic Eye Disease (DED) stands as a significant contributor to vision loss. Diverse methodologies have emerged to forecast its progression and accurately assess the various stages. Machine Learning (ML) and Deep Learning (DL) algorithms have become essential tools in this endeavor, primarily through their adept analysis of Diabetic Retinopathy (DR) images. However, there is still a need for a more efficient and accurate method to predict DR performance. It developed an innovative method for classifying and predicting diabetic retinopathy. The novel idea in this research is to combine several techniques, including ensemble learning and a 2D convolutional neural network; we utilized transfer learning and a correlation method in our approach. Initially, the Stochastic Gradient Boosting process was employed to predict diabetic retinopathy.