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Autogenous diabetic retinopathy censor for ophthalmologists ??? AKSHI

机译:眼科医生的自生糖尿病视网膜病审查员??? Akshi.

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The Diabetic Retinopathy (DR) is a rapidly growing interrogation around the world which can be annotated by abortive metabolism of glucose that causes long-term infection in human retina. This is one of the preliminary reason of visual impairment and blindness of adults. Information on retinal pathological mutation can be recognized using ocular fundus images. In this research, we are mainly focused on resurrecting an automated diagnosis system to detect DR anomalies such as severity level classification of DR patient (Non-proliferative Diabetic Retinopathy approach) and vessel tortuosity measurement of untwisted vessels to assessment of vessel anomalies (Proliferative Diabetic Retinopathy approach). Severity classification method is obtained better results according to the precision, recall, F-measure and accuracy (exceeds 94%) in all formats of cross validation. In ROC (Receiver Operating Characteristic) curves also visualized the higher AUC (Area Under Curve) percentage (exceeds 95%). User level evaluation of severity capturing is obtained higher accuracy (85%) result and fairly better values for each evaluation measurements. Untwisted vessel detection for tortuosity measurement also carried out the good results with respect to the sensitivity (85%), specificity (89%) and accuracy (87%).
机译:糖尿病视网膜病变(DR)是围绕世界迅速增长的审讯,其可以通过葡萄糖的含量血糖的代谢来注释,这导致人类视网膜的长期感染。这是成年人视觉损害和失明的初步原因之一。可以使用眼底图像识别视网膜病理突变的信息。在这项研究中,我们主要集中于复活自动诊断系统,以检测DR患者博士患者的严重程度分类(非增殖性糖尿病视网膜病变)和血管曲折测量血管异常的血管曲折测量(增殖性糖尿病视网膜病变方法)。在所有格式的交叉验证中,根据精度,召回,F测量和精度(超过94%)获得了严重性分类方法。在ROC(接收器操作特性)曲线中,曲线还可视化较高的AUC(曲线区域)百分比(超过95%)。对严重性捕获的用户级评估是获得更高的精度(85%)结果和每个评估测量的相当更好的值。无捻血管检测对于曲折测量也对敏感性(85%),特异性(89%)和精度(87%)进行了良好的效果。

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