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Effective methods of diabetic retinopathy detection based on deep convolutional neural networks

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Diabetic retinopathy (DR) has become the leading cause of blindness worldwide. In clinical practice, the detection of DR often takes a lot of time and effort for ophthalmologist. It is necessary to develop an automatic assistant diagnosis method based on medical image analysis techniques.

Methods

Firstly, we design a feature enhanced attention module to capture focus lesions and regions. Secondly, we propose a stage sampling strategy to solve the problem of data imbalance on datasets and avoid the CNN ignoring the focus features of samples that account for small parts. Finally, we treat DR detection as a regression task to keep the gradual change characteristics of lesions and output the final classification results through the optimization method on the validation set.

Results

Extensive experiments are conducted on open-source datasets. Our methods achieve 0.851 quadratic weighted kappa which outperforms first place in the Kaggle DR detection competition based on the EyePACS dataset and get the accuracy of 0.914 in the referable/non-referable task and 0.913 in the normal/abnormal task based on the Messidor dataset.

Conclusion

In this paper, we propose three novel automatic DR detection methods based on deep convolutional neural networks. The results illustrate that our methods can obtain comparable performance compared with previous methods and generate visualization pictures with potential lesions for doctors and patients.

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Acknowledgements

This research is supported by National Key R&D Program of China (No. 2018YFC0115102), National Natural Science Foundation of China (Nos. 61872020, U20A20195), Beijing Natural Science Foundation Haidian Primitive Innovation Joint Fund (L182016), Beijing Advanced Innovation Center for Biomedical Engineering (ZF138G1714), Research Unit of Virtual Human and Virtual Surgery, Chinese Academy of Medical Sciences (2019RU004), Shenzhen Research Institute of Big Data, Shenzhen, 518000. We also thank the Faculty of Media and Communication, Bournemouth University (UK) with its support of Global Visiting Fellowship for Dr. Junjun Pan.

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Authors and Affiliations

  1. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China

    Yunchao Gu, Xinliang Wang, Junjun Pan, Zhifan Yong, Tianze Pan & Zhong Zhou

  2. Peng Cheng Laboratory, Shenzhen, 518000, China

    Junjun Pan

  3. Hangzhou Innovation Research Institute, Beihang University, Hangzhou, 100191, China

    Yunchao Gu

  4. Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC), Beihang University, Beijing, 100191, China

    Yunchao Gu

  5. School of Informatics, Xiamen University, Xiamen, 361005, China

    Shihui Guo

  6. Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China

    Yonghong Jiao

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  1. Yunchao Gu
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  2. Xinliang Wang
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Correspondence to Junjun Pan.

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The authors declare that they have no conflict of interest and personal relationships with other people or organizations that can inappropriately influence this work. There is no professional or other personal interest of any nature or kind in any product, service, and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.

Ethical approval

The data we used are open-source fundus color images, so there are no animal and human experiments.

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Gu, Y., Wang, X., Pan, J. et al. Effective methods of diabetic retinopathy detection based on deep convolutional neural networks. Int J CARS 16, 2177–2187 (2021). https://doi.org/10.1007/s11548-021-02498-8

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