Early Segmentation and Voice Alert Management System for Diabetic Retinopathy Detection
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Abstract
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.