Diabetic retinopathy (DR) is an optical syndrome infecting the eyes’ vision by impairing the retinal blood vessels. Early misdetection of impairment results in hemorrhage, a state in which retinal bleeding occurs. Therefore, initial detection of such bleeding in the retina is identified using intelligent computing and clinical analysis. This analysis helps to improve the precision of detection and requires complex-less time and processing instances. In this article, the regression model for retina feature filtering (RM-FF) is introduced to improve the accuracy of detecting hemorrhages. In this filtering, the complex image is simplified into smaller blocks for classification and conditional verification. Based on conditional verification, the training set is updated recursively to improve the specificity and sensitivity detection process. Using a differential dataset, the proposed detection method assessed using the metrics true positive rate, accuracy, sensitivity, and specificity.

中文翻译：

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基于回归模型的特征过滤提高糖尿病视网膜病变治疗中出血检测的准确性

糖尿病视网膜病变 (DR) 是一种通过损害视网膜血管来感染眼睛视力的光学综合征。早期错误检测损伤会导致出血，这是一种发生视网膜出血的状态。因此，使用智能计算和临床分析识别视网膜出血的初步检测。这种分析有助于提高检测的精度，并且需要更简单的时间和处理实例。本文介绍了视网膜特征过滤（RM-FF）的回归模型，以提高检测出血的准确性。在这种过滤中，复杂的图像被简化为更小的块，用于分类和条件验证。基于条件验证，递归更新训练集以提高特异性和敏感性检测过程。