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Categorization of Diabetic Retinopathy and Identification of Characteristics to Assist Effective Diagnosis

机译：糖尿病患者的分类和识别特征，以促进有效诊断

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Diabetic Retinopathy is a condition caused as a result of damage to the blood vessels in the retina due to poor blood sugar control. Diabetes results in a gradual deterioration of the other organs firstly the retina thus causing adverse effects to vision. However, this damage can be prevented or treated if identified in its initial stages which is possible if monitored on a timely basis. The methodology proposed in this paper consists of two components: the classification component and the feature extraction component. The classification component deals with the utilization of transfer learning that includes a CNN model pre-trained on the ImageNet dataset retrained on the retinal dataset to develop a model that most effectively categorizes diabetic retinopathy based on the level of severity. The feature extraction component further extracts characteristics using appropriate image processing libraries. Such an easily accessible system is also capable of most effectively assisting the diagnosis of retinopathy especially in rural areas where instruments to detect such diseases are rarely available.
机译：糖尿病视网膜病变是由于血糖控制不良导致视网膜血管的损坏导致的病症。糖尿病首先导致其他器官的逐渐劣化，从而导致对视觉的不利影响。然而，如果在其初始阶段识别，则可以防止或处理这种损坏，该阶段是及时监测的。本文提出的方法包括两个组件：分类组件和特征提取组件。分类组件处理转移学习的利用，该传输学习包括预先培训的CNN模型，在视网膜数据集上再培养的想象，以发展基于严重程度的级别有效地分类糖尿病视网膜病变的模型。特征提取组件进一步提取使用适当的图像处理库提取特性。这种易于访问的系统也能够最有效地帮助诊断视网膜病变，特别是在检测此类疾病的仪器的农村地区很少。

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