
Proceedings Paper
SVM classification of microaneurysms with imbalanced dataset based on borderline-SMOTE and data cleaning techniquesFormat | Member Price | Non-Member Price |
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Paper Abstract
Microaneurysms are the earliest clinic signs of diabetic retinopathy, and many algorithms were developed for the automatic classification of these specific pathology. However, the imbalanced class distribution of dataset usually causes the classification accuracy of true microaneurysms be low. Therefore, by combining the borderline synthetic minority over-sampling technique (BSMOTE) with the data cleaning techniques such as Tomek links and Wilson’s edited nearest neighbor rule (ENN) to resample the imbalanced dataset, we propose two new support vector machine (SVM) classification algorithms for the microaneurysms. The proposed BSMOTE-Tomek and BSMOTE-ENN algorithms consist of: 1) the adaptive synthesis of the minority samples in the neighborhood of the borderline, and 2) the remove of redundant training samples for improving the efficiency of data utilization. Moreover, the modified SVM classifier with probabilistic outputs is used to divide the microaneurysm candidates into two groups: true microaneurysms and false microaneurysms. The experiments with a public microaneurysms database shows that the proposed algorithms have better classification performance including the receiver operating characteristic (ROC) curve and the free-response receiver operating characteristic (FROC) curve.
Paper Details
Date Published: 17 March 2017
PDF: 7 pages
Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103411S (17 March 2017); doi: 10.1117/12.2268519
Published in SPIE Proceedings Vol. 10341:
Ninth International Conference on Machine Vision (ICMV 2016)
Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou, Editor(s)
PDF: 7 pages
Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103411S (17 March 2017); doi: 10.1117/12.2268519
Show Author Affiliations
Jiayi Wu, Xi'an Jiaotong Univ. (China)
Nanning Zheng, Xi'an Jiaotong Univ. (China)
Nanning Zheng, Xi'an Jiaotong Univ. (China)
Published in SPIE Proceedings Vol. 10341:
Ninth International Conference on Machine Vision (ICMV 2016)
Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou, Editor(s)
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