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A Deep Learning Based Pipeline for Image Grading of Diabetic Retinopathy

  • Yu WangEmail author
  • G. Alan Wang
  • Weiguo Fan
  • Jiexun Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10983)

Abstract

Diabetic Retinopathy (DR) is one of the principal sources of blindness due to diabetes mellitus. It can be identified by lesions of the retina, namely, microaneurysms, hemorrhages, and exudates. DR can be effectively prevented or delayed if discovered early enough and well-managed. Prior image processing studies on diabetic retinopathy typically extract features manually but are time-consuming and not accurate. In this research, we propose a research framework using advanced retina image processing, deep learning, and boosting algorithm for high-performance DR grading. First, we preprocess the retina image datasets to highlight signs of DR, then employ a convolutional neural network to extract features of retina images, and finally apply a boosting tree algorithm to make a prediction. Experimental results show that our pipeline has excellent performance when grading diabetic retinopathy score on Kaggle dataset.

Keywords

Retina image Image grading Diabetic retinopathy Early detection Feature extraction ConvNN Deep learning Boosting tree 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceVirginia TechBlacksburgUSA
  2. 2.Department of Business Information TechnologyVirginia TechBlacksburgUSA
  3. 3.Department of Accounting and Information SystemsVirginia TechBlacksburgUSA
  4. 4.Department of Decision SciencesWestern WashingtonBellinghamUSA

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