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Generative Adversarial Networks and Improved Efficientnet for Imbalanced Diabetic Retinopathy Grading

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Cognitive Systems and Information Processing (ICCSIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1515))

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Abstract

Diabetic retinopathy (DR) is a complication of diabetes and one of the causes of blindness and visual impairment. Automated and accurate DR grading is of great significance for timely and effective treatment of fundus diseases. At present, traditional convolutional neural networks (CNNs) will pay too much atteneion to the DR level with a lot of samples because of the unbalanced data distribution. So unbalanced data distribution will affect the classification ability of the model. In This paper, Double-Generator-Efficientnet Network (DGENet) based on Efficientnet is proposed to effectively improve the grading ability of DR, which consists of the following: (1) The first network classifies No DR and DR to solve the problem that No DR occupies most of the data distribution. (2) The second network divides the DR in the first network into mild, moderate, severe non-proliferative DR (NPDR) and proliferative DR(PDR). The second add the generative network from Deep Convolutional Generative Adversarial Networks (DCGAN) for data enhancement to solve the problem of imbalanced data in the four levels of DR. (3) Add attention mechanism modules to the four-level network. Experimental results show that the proposed DGENet outperforms some new methods in the publicly available Kaggle and Messidor-2 fundus image datasets, especially on the four levels of DR1 to DR4.

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Acknowledgments

This work was supported by the Natural Science Foundation of Shanxi Province (201801D121144) and the Natural Science Foundation of Shanxi Province (201901D211079).

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Authors and Affiliations

  1. College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, 030024, China

    Kaifei Zhao, Wentao Zhao, Zhe Zhang & Xinying Xu

  2. College of Information and Computer, Taiyuan University of Technology, Jinzhong, 030600, China

    Jun Xie & Binrong Li

Authors
  1. Kaifei Zhao
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  2. Wentao Zhao
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  3. Jun Xie
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  4. Binrong Li
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  5. Zhe Zhang
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  6. Xinying Xu
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Corresponding author

Correspondence to Xinying Xu .

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Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fuchun Sun

  2. National University of Defense Technology, Changsha, China

    Dewen Hu

  3. Universität Hamburg, Hamburg, Germany

    Stefan Wermter

  4. Tsingzhan Artificial Intelligence Research Institute, Nanjing, China

    Lei Yang

  5. Tsinghua University, Beijing, China

    Huaping Liu

  6. Tsinghua University, Beijing, China

    Bin Fang

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Zhao, K., Zhao, W., Xie, J., Li, B., Zhang, Z., Xu, X. (2022). Generative Adversarial Networks and Improved Efficientnet for Imbalanced Diabetic Retinopathy Grading. In: Sun, F., Hu, D., Wermter, S., Yang, L., Liu, H., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2021. Communications in Computer and Information Science, vol 1515. Springer, Singapore. https://doi.org/10.1007/978-981-16-9247-5_27

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  • DOI: https://doi.org/10.1007/978-981-16-9247-5_27

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  • Online ISBN: 978-981-16-9247-5

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