论文标题
基于视网膜图像使用集合学习的早期失明检测
Early Blindness Detection Based on Retinal Images Using Ensemble Learning
论文作者
论文摘要
糖尿病性视网膜病（DR）是世界各地成年人视力丧失的主要原因。在五个病例中，有四个病例长期患有糖尿病，导致DR。如果早点检测到，则可以通过适当的治疗来防止超过90％的新DR事件变成失明。尽管有多种可以处理DR的治疗程序，但早期检测的疏忽和失败使大多数DR患者的视力造成了宝贵的视力。在这方面，数字图像处理（DIP）和机器学习（ML）领域（DIP）和机器学习（ML）的最新发展已铺平了在这方面使用机器的方法。当代技术使我们能够开发能够根据其视网膜图像自动检测眼睛的状况的设备。但是，实际上，几个因素阻碍了捕获的图像的质量并阻碍检测结果。在这项研究中，已经根据使用集合学习算法从视网膜图像中提取的颜色信息提出了一种新型的早期盲目检测方法。该方法已在南亚农村地区的人们收集的一组视网膜图像上进行了测试，从而产生了91％的分类准确性。
Diabetic retinopathy (DR) is the primary cause of vision loss among grownup people around the world. In four out of five cases having diabetes for a prolonged period leads to DR. If detected early, more than 90 percent of the new DR occurrences can be prevented from turning into blindness through proper treatment. Despite having multiple treatment procedures available that are well capable to deal with DR, the negligence and failure of early detection cost most of the DR patients their precious eyesight. The recent developments in the field of Digital Image Processing (DIP) and Machine Learning (ML) have paved the way to use machines in this regard. The contemporary technologies allow us to develop devices capable of automatically detecting the condition of a persons eyes based on their retinal images. However, in practice, several factors hinder the quality of the captured images and impede the detection outcome. In this study, a novel early blind detection method has been proposed based on the color information extracted from retinal images using an ensemble learning algorithm. The method has been tested on a set of retinal images collected from people living in the rural areas of South Asia, which resulted in a 91 percent classification accuracy.
