Lesion Detection and Grading of Diabetic Retinopathy via Two-Stages Deep Convolutional Neural Networks

  • Yehui YangEmail author
  • Tao Li
  • Wensi Li
  • Haishan Wu
  • Wei Fan
  • Wensheng Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)


We propose an automatic diabetic retinopathy (DR) analysis algorithm based on two-stages deep convolutional neural networks (DCNN). Compared to existing DCNN-based DR detection methods, the proposed algorithm has the following advantages: (1) Our algorithm can not only point out the lesions in fundus color images, but also give the severity grades of DR. (2) By introducing an imbalanced weighting scheme, more attentions will be payed on lesion patches for DR grading, which significantly improves the performance of DR grading under the same implementation setup. In this study, we label 12, 206 lesion patches and re-annotate the DR grades of 23, 595 fundus images from Kaggle competition dataset. Under the guidance of clinical ophthalmologists, the experimental results show that our lesion detection net achieves comparable performance with trained human observers, and the proposed imbalanced weighted scheme also be proved to significantly enhance the capability of our DCNN-based DR grading algorithm.


Diabetic retinopathy Deep convolutional neural networks Fundus images Retinopathy lesions 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yehui Yang
    • 1
    • 2
    Email author
  • Tao Li
    • 2
  • Wensi Li
    • 3
  • Haishan Wu
    • 4
  • Wei Fan
    • 1
  • Wensheng Zhang
    • 2
  1. 1.Big Data Lab, Baidu ResearchBeijingChina
  2. 2.Institute of Automation, Chinese Academy of SciencesBeijingChina
  3. 3.The Beijing Moslem’s HospitalBeijingChina
  4. 4.Heyi VenturesBeijingChina

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