基于多尺度区域块的糖尿病性视网膜病变级联检测
Cascade detection of diabetic retinopathy based on multi-scale region blocks

摘要 目前，基于形态学检测糖尿病性视网膜病变的算法复杂度较高，深度学习直接逐像素检测的方法虽然能够避免排除生理结构、手动设计特征的不足，但计算量大、分割速度较慢.为了解决上述问题，提出一种基于深度学习的级联检测框架.首先，分块检测眼底图像是否存在病变区域，然后对病变区域进行逐像素的分割，从而检测出微动脉瘤、出血点、硬渗出及软渗出4种病变.实验结果表明：在眼底图像公开数据库DIARETDB1中，4种病变的检测率分别为88.62%、94.91%、9891%和92.91%.与形态学方法相比，微动脉瘤和出血点检测精度分别提高了17.39%和15.18%；与直接逐像素检测方法相比，平均检测时间仅为原来的1/4.
Abstract：At present, morphological methods for detecting diabetic retinopathy have high complexity.The traditional deep learning methods avoid the exclusion of physiological structure, manual design features.However, they have to do large calculation and the speed is relatively slow. In order to solve these problems, this paper presents a cascade detection framework based on deep learning. Firstly, the fundus images are divided into blocks to detect whether there are lesions, and then pixels in these lesions are classified into four categories：microaneurysms, haemorrhages, hard exudates and soft exudates. The experimental results show that on the public DIARETDB1 fundus image database, the detection of four kinds lesions with sensitivity are 88.62%, 94.91%, 98.91% and 92.91%, respectively.Compared to morphological methods, the accuracy has improved 17.39% in microaneurysms and 15.18% in haemorrhages. And the detection time is only a quarter of the traditional deep learning methods.