Diabetic Retinopathy (DR) is one of the most common diabetic diseases that affect the retina’s blood vessels. Too much of the glucose level in blood leads to blockage of blood vessels in the retina, weakening and damaging the retina. Automatic classification of diabetic retinopathy is a challengingtask in medical research. This work proposes a Mixture of Ensemble Classifiers (MEC) to classify and grade diabetic retinopathy images using hierarchical features. We use an ensemble of classifiers such as support vector machine, random forest, and Adaboost classifiers that use the hierarchical feature maps obtained at every pooling layer of a convolutional neural network (CNN) for training. The feature maps are generated by applying the filters to the output of the previous layer. Lastly, we predict the class label or the grade for the given test diabetic retinopathy image by considering the class labels of all the ensembled classifiers. We have tested our approaches on the E-ophtha dataset for the classification task and the Messidor dataset for the grading task. We achieved an accuracy of 95.8% and 96.2% for the E-ophtha and Messidor datasets, respectively. A comparison among prominent convolutional neural network architectures and the proposed approach is provided.

中文翻译：

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使用集成分类器的混合对糖尿病视网膜病变图像进行分类和分级

糖尿病视网膜病变 (DR) 是影响视网膜血管的最常见的糖尿病疾病之一。血液中过多的葡萄糖水平会导致视网膜血管阻塞，削弱和损害视网膜。糖尿病视网膜病变的自动分类是医学研究中的一项具有挑战性的任务。这项工作提出了一种组合分类器 (MEC) 来使用分层特征对糖尿病视网膜病变图像进行分类和分级。我们使用一组分类器，例如支持向量机、随机森林和 Adaboost 分类器，这些分类器使用在卷积神经网络 (CNN) 的每个池化层获得的分层特征图进行训练。特征图是通过将过滤器应用于前一层的输出来生成的。最后，我们通过考虑所有集成分类器的类标签来预测给定测试糖尿病视网膜病变图像的类标签或等级。我们已经在用于分类任务的 E-ophtha 数据集和用于分级任务的 Messidor 数据集上测试了我们的方法。我们对 E-ophtha 和 Messidor 数据集的准确率分别为 95.8% 和 96.2%。提供了突出的卷积神经网络架构与所提出的方法之间的比较。