Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 203-207.

• Pattem Recognition & Image Processing • Previous Articles     Next Articles

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

CAI Zhen-zhen, TANG Peng, HU Jian-bin, JIN Wei-dong   

  1. Southwest Jiaotong University,Chengdu 610036,China
  • Online:2019-02-26 Published:2019-02-26

Abstract: 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.

Key words: Convolutional neural network, Diabetic retinopathy, Hard exudates, Probability graph, Pseudo-color map

CLC Number: 

  • TP181
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