title: An Active Learning Classifier for Further Reducing Diabetic Retinopathy Screening System Cost.
abstract: 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, andKNearest Neighbor. Empirical experiments suggest that our active learning classifier is efficient for further reducing DR screening cost.
authors.affiliation: School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China; College of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300222, China.
authors.forename: Yinan
authors.identifier: 0000-0001-5970-7308
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: College of Science, Tianjin University of Science and Technology, Tianjin 300222, China.
authors.forename: Mingqiang
authors.initials: M
authors.lastname: An