Early Segmentation and Voice Alert Management System for Diabetic Retinopathy Detection

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K.T.Ilayarajaa, E.Logashanmugam


Diabetic Retinopathy (DR) is extracted under later stages of vision blur or complete blindness or vision loss. The diabetic retinopathy resultant causes an irreversible blindness and hence requires a detailed attention and early detection framework. In this article, we propose a machine learning approach for early segmentation and classification of diabetic retinopathy using pre-morbidities and patient history with respect to diabetics. Typically, a uncontrolled diabetic lead to retinopathy formation. The proposed technique classifies the possibilities of occurrences with feature and attributes of electronics health records (EHR) systems. The proposed technique includes multiple data-dimensions and attributes. Both segmentation and classification of attributes and feature-sets are cross validated via an inter-dependency mapping of attributes. The technique uses primary-dependencies attributes such as diabetics levels post and pre fasting, history of diabetic treatment and retinopathy aging with reference to aligned ill’ment. The technique has successfully classified with a performance ratio of 97.81% with respect to aimed parameter evaluation. The process has also retained a voice based monitoring and alerting system.

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How to Cite
K.T.Ilayarajaa, E.Logashanmugam. (2021). Early Segmentation and Voice Alert Management System for Diabetic Retinopathy Detection. Annals of the Romanian Society for Cell Biology, 7915 –. Retrieved from https://annalsofrscb.ro/index.php/journal/article/view/3451