Abstract
Diabetic retinopathy disease is one of the notorious metabolic disorders happens due to increase of blood sugar level in human body. In computer vision, images are recognized as the indispensable tool for precise prediction and diagnosis of diabetic retinopathy. Therefore, the proposed research study considers the fundus images of various patients containing the diabetic disease. Basic idea behind this research is to introduce a stochastic neighbor embedding (SNE) feature extraction approach for the sake of dimensional reduction and unnecessary noise removal from the fundus images. After feature extraction, the proposed optimized deep belief network (O-DBN) classifier model is capable of measuring the image features into various classes that gives the severity levels of diabetic retinopathy disease. Moreover, the proposed cloud-enabled diabetic retinopathy prediction system using the SNE feature extraction and O-DBN classification model could outperform the existing online prediction systems in terms of sensitivity, specificity, F1-score, prediction time and accuracy.
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Data Availability
The datasets analyzed during the current study are available in the Mendeley Data repository [https://data.mendeley.com/datasets/3csr652p9y/1].
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Rajavel, R., Sundaramoorthy, B., GR, K. et al. Cloud-enabled Diabetic Retinopathy Prediction System using optimized deep Belief Network Classifier. J Ambient Intell Human Comput 14, 14101–14109 (2023). https://doi.org/10.1007/s12652-022-04114-2
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DOI: https://doi.org/10.1007/s12652-022-04114-2