基于深度学习的眼底图像出血点检测方法

提出了一种基于卷积神经网络(convolutional neural networks，CNN)加条件随机场(conditional random fields，CRF)的眼底图像出血点检测方法。首先，为了避免图像背景区域对后续检测的影响，参考眼底图像中的灰度信息并根据眼底中心位置到其边缘的长度，将图像调整到合适的尺寸，再对图像进行线性加权增强其亮度和对比度；然后，用裁剪到的图像块在仿照VGG网络构建的CNN架构上去训练检测出血点的CNN模型；最后，为了克服CNN模型在出血点检测中误检、漏检等问题，采用CRF对CNN模型输出的概率图进行后处理，以实现眼底图像出血点的精确检测。提出的检测方法在公开的Kaggle与Messidor数据库上进行训练和验证，获得了98.8%的准确率、99.4%的召回率和99.1%的F-score。另外，在DIARETDB1数据库上测试的灵敏度达到98.5%，F-score为96.1%。实验结果表明，从图像视觉和定量检测2个方面均说明了提出方法的有效性和优越性。

Detection method of hemorrhages of fundus image based on deep learning

This paper proposes a method for detecting the bleeding point of fundus images based on convolutional neural networks (CNN) plus conditional random fields (CRF). First, in order to avoid the influence of the background area of the image on subsequent detection, refer to the gray level information in the fundus image and adjust the image to the appropriate size according to the length of the fundus center to its edge, and then linearly weight the image to enhance its brightness and contrast; then, using the cropped image block to train the CNN model for detecting the bleeding point on the CNN architecture built on the VGG network; finally, to overcome the problems of false detection and missed detection in the bleeding point detection of the CNN model, CRF is used for CNN. The probability map of the model output is post-processed to achieve accurate detection of the bleeding point of the fundus image. The detection method proposed in this paper was trained and verified on the open Kaggle and Messidor databases, and achieved 98.8% accuracy, 99.4% recall rate and 99.1% F-score. In addition, the sensitivity tested on the DIARETDB1 database reached 98.5% and the F-score was 96.1%. The experimental results show that the effectiveness and superiority of the proposed method are illustrated from both image visual and quantitative detection.