论文标题
通过眼底摄影对糖尿病性视网膜病的分类：深度学习方法的利用来加快疾病检测
Classification of Diabetic Retinopathy via Fundus Photography: Utilization of Deep Learning Approaches to Speed up Disease Detection
论文作者
论文摘要
在本文中，我们针对糖尿病性视网膜病（DR）分类问题提出了两种不同的解决方案。在第一种方法中，我们引入了浅神经网络体系结构。该模型在最频繁的类的分类上表现良好，而在分类较低的类别方面失败了。在第二种方法中，我们使用转移学习来重新培训非常深的神经网络的最后修改层，以提高模型对较不频繁类的概括能力。我们的结果表明，与浅层神经网络相比，与较低的类别的DR分类中转移学习的能力优越。
In this paper, we propose two distinct solutions to the problem of Diabetic Retinopathy (DR) classification. In the first approach, we introduce a shallow neural network architecture. This model performs well on classification of the most frequent classes while fails at classifying the less frequent ones. In the second approach, we use transfer learning to re-train the last modified layer of a very deep neural network to improve the generalization ability of the model to the less frequent classes. Our results demonstrate superior abilities of transfer learning in DR classification of less frequent classes compared to the shallow neural network.
