深度卷积神经网络实现硬性渗出的自动检测

为实现硬性渗出的自动检测,构建糖网病计算机辅助诊断系统,文中提出了一种基于深度卷积神经网络的硬性渗出提取方法。该方法主要分为两个部分:线下训练硬性渗出分类模型和在线检测硬性渗出。线下训练分类模型是利用深度卷积神经网络自动提取特征训练出硬性渗出的分类模型；在线检测硬性渗出使用训练好的分类模型对眼底影像中的硬性渗出进行检测,并获取硬性渗出的概率图以及伪彩色图。利用文中方法在标准数据集DIARETDB1和自选数据集上进行验证,结果表明所提方法行之有效,鲁棒性较好,具有很强的临床实践意义。

Auto-detection of Hard Exudates Based on Deep Convolutional Neural Network

A hard exudates (HEs) detection method based on deep convolution neural network was proposed in this paper,which achieves the purpose of automatic detection for HEs and contributes to the creation of diabetic retinopathy (DR) computer-aided diagnostic system.This method includes training the classification model for HEs offline and detection for HEs online.In order to train HEs classification model offline,CNN is adopted to extract HEs features automatically.Then,HEs in fundus image are detected by HEs classification model which has been trained offline,meanwhile,HEs probability graph and HEs pseudo-color map are obtained.The method was verified on standard data set and self-built data set respectively.Compared with other methods,the proposed method is profitable with strong robustness,and has very strong clinical practice significance.