Now a day’s intelligent diagnoses approaches are massively accepted for the purpose of advance analysis and detection of several diseases. In this work a supervised learning based approach using artificial neural network (ANN) has been proposed to achieve more accurate diagnoses outcomes for the case of diabetic retinopathy. Features extracted from the retina images are used as input to the ANN based classifier. Customized ANN architecture by estimating several entities of traditional ANN has been used to improve the accuracy of the method. The ANN architecture used in this work is feed forward back propagation neural network. Accuracy obtained for the proposed method is found to be 97.13%. The results suggest that proposed method can be used to detect diabetic retinopathy effectively.

中文翻译：

---

一种采用监督学习技术的糖尿病视网膜病变检测的改进方法

现在，每天的智能诊断方法已被广泛接受，以进行多种疾病的提前分析和检测。在这项工作中，已经提出了一种使用人工神经网络（ANN）的基于监督学习的方法，以实现针对糖尿病性视网膜病的更准确的诊断结果。从视网膜图像中提取的特征用作基于ANN的分类器的输入。通过估计传统ANN的几个实体的定制ANN体系结构已用于提高该方法的准确性。在这项工作中使用的ANN架构是前馈传播神经网络。发现该方法的准确性为97.13％。结果表明所提出的方法可以有效地检测糖尿病性视网膜病变。