Cloud-based onboard prediction and diagnosis of diabetic retinopathy

Diabetic retinopathy remains the most daunting microvascular complication of diabetic disease. Major risk factors of diabetic retinopathy include long-term diabetics, improper blood glucose level, and dyslipidemia. In order to investigate the presence of diabetic retinopathy-related features and to deal with the diagnosis of various diabetic severity stages, there is a need for a cloud-based automated screening system. So, the development of novel biomarkers should take the advantage of objective measurement and evaluation indicating the biological and pathogenic processes to all the therapeutic interventions. Current state-of-the-art research studies lack accommodating large sample size, cross-validation among the diverse populations, time, and cost-effective diagnosis techniques. To overcome these challenges, a cloud-based diabetic retinopathy prediction system is proposed using the recurrent convolution neural network classifier model. The proposed prediction system can provide risk stratification, optimized resource allocation, a severe form of disease prediction, risk control, and preventive interventions during the screening and diagnosis process. It also provides prognostic information to patients stating various cost-effective predictive responses according to treatment modalities. Moreover, the proposed system clinical decision-making will outperform the existing classifiers in terms of prediction accuracy, sensitivity, specificity, precision, recall, and F1-score.