Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (3): 144-149.DOI: 10.3778/j.issn.1002-8331.1910-0413

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Glaucoma Fundus Images Classification Method Based on Transfer Learning

XU Zhijing, WANG Yi   

  1. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
  • Online:2021-02-01 Published:2021-01-29



  1. 上海海事大学 信息工程学院,上海 201306


In view of the lack of large-scale publicly marked glaucoma data set, in order to solve the problems of insufficient learning ability of small samples and low classification accuracy, a glaucoma fundus image recognition system based on transfer learning is proposed. The obtained glaucoma fundus image is preprocessed, such as denoising, deleting redundant background, Extracting Region of Interest(ROI), image enhancement and so on. Then, based on the VGG16 network, the fully connected layer is redesigned to obtain a simplified deep neural network model Reduce-VGGNet(R-VGGNet). In the training process of R-VGGNet network, the convolution layer and pooling layer inherit the VGG16 model and pre-train the weight parameters on the ImageNet data set, and the parameters of the fully connected layer are adaptively adjusted according to the glaucoma data set. Finally, different network structures and different training strategies are tested for performance and comparative tests of different classification methods. The experimental results show that the recognition method based on R-VGGNet network model improves the accuracy of distinguishing glaucoma patients. The accuracy rate can reach to 91.7%, which provides a good solution for the diagnosis and treatment of clinicians.

Key words: glaucoma, transfer learning, extracting Region of Interest(ROI) network, VGG16 network, Reduce-VGGNet(R-VGGNet)



关键词: 青光眼, 迁移学习, 提取感兴趣区域(ROI), VGG16网络, 简化的深度神经网络(R-VGGNet)