关键词: 深度学习, 目标检测, 卷积神经网络, 计算机视觉, 糖尿病眼底病变

Abstract:

Diabetic Retinopathy（DR） is a leading cause of blindness. In order to diagnose diabetic retinopathy efficiently, deep learning algorithm is applied to detect diabetic retinopathy images. As the plain convolution neural networks can only classify images and the scale of input must be fixed, Region-based Fully Convolutional Networks（R-FCN） object detection algorithm is proposed to classify retinal fundus images and detect lesion regions with any scales. To solve the problem that the original R-FCN algorithm is difficult to detect small objects （minimal retinal hemorrhage and microaneurysm）, the R-FCN algorithm is modified by adding the FPN （Feature Pyramid Networks）, upgrading the backbone network, and modifying the RPN （Region Proposal Network）. The results show that the modified R-FCN algorithm can achieve high accuracy in the classification of retinal fundus images （healthy, mild, moderate, severe and proliferative） and the detection of lesion regions （microaneurysm, retinal hemorrhage and vitreous hemorrhage）.

Key words: deep learning, object detection, convolution neural networks, computer vision, diabetic retinopathy