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Computational and Mathematical Methods in Medicine
Volume 2016 (2016), Article ID 4345936, 10 pages
Research Article

An Active Learning Classifier for Further Reducing Diabetic Retinopathy Screening System Cost

1School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
2College of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300222, China
3College of Science, Tianjin University of Science and Technology, Tianjin 300222, China

Received 7 May 2016; Revised 24 June 2016; Accepted 26 July 2016

Academic Editor: Georgy Gimel’farb

Copyright © 2016 Yinan Zhang and Mingqiang An. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Diabetic retinopathy (DR) screening system raises a financial problem. For further reducing DR screening cost, an active learning classifier is proposed in this paper. Our approach identifies retinal images based on features extracted by anatomical part recognition and lesion detection algorithms. Kernel extreme learning machine (KELM) is a rapid classifier for solving classification problems in high dimensional space. Both active learning and ensemble technique elevate performance of KELM when using small training dataset. The committee only proposes necessary manual work to doctor for saving cost. On the publicly available Messidor database, our classifier is trained with 20%–35% of labeled retinal images and comparative classifiers are trained with 80% of labeled retinal images. Results show that our classifier can achieve better classification accuracy than Classification and Regression Tree, radial basis function SVM, Multilayer Perceptron SVM, Linear SVM, and Nearest Neighbor. Empirical experiments suggest that our active learning classifier is efficient for further reducing DR screening cost.