Identification of Early Diabetic Retinopathy Through Outlier Based Skimpy Regularization Fuzzy Clustering with Meta-Cognitive Neural Network

Background: Traditional screening methods face limitations in terms of efficiency and accuracy, prompting the explorationTraditional screening methods for diabetic retinopathy often struggle with efficiency and accuracy. To address these challenges, we have explored advanced technologies like outlier-based Skimpy Regularization Fuzzy Clustering and the Meta- Cognitive Neural Network (McNN). By integrating artificial intelligence and sophisticated image processing techniques, our intelligent screening system aims to significantly enhance both the speed and precision of diabetic retinopathy diagnosis, enabling timely intervention for at-risk individuals. Methods: Our analysis compared various classifiers, including Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), and the proposed McNN. Findings: The McNN classifier outperformed the others, demonstrating exceptional performance with a specificity of 93.90%, sensitivity of 99.06%, and an overall accuracy of 97.89%. Notably, the McNN achieved a Positive Predictive Value (PPV) of 100%, indicating flawless performance in predicting positive cases. These results underscore the potential of advanced AI technologies to revolutionize diabetic retinopathy screening by improving diagnostic accuracy and efficiency. results: Early detection is crucial in mitigating the impact of diabetic retinopathy, ultimately reducing the risk of irreversible vision loss in diabetic patients and alleviating the burden on global healthcare systems. The significance of the McNN classifier's performance, reinforcing the importance of advanced AI in early diabetic retinopathy detection and intervention.