An automated grading system for diabetic retinopathy using curvelet transform and hierarchical classification
Fanji Ari MuktiCentre for Visual Computing, Faculty of Computing and Informatics, Multimedia University, Cyberjaya, MalaysiaC. EswaranCentre for Visual Computing, Faculty of Computing and Informatics, Multimedia University, Cyberjaya, MalaysiaNoramiza HashimCentre for Visual Computing, Faculty of Computing and Informatics, Multimedia University, Cyberjaya, MalaysiaChiung Ching HoCentre for Visual Computing, Faculty of Computing and Informatics, Multimedia University, Cyberjaya, MalaysiaMohamed Uvaze Ahamed Ayoobkhan
2018en
ABI
Abstract
In this paper, an automated system for grading the severity level of Diabetic Retinopathy (DR) disease based on fundus images is presented. Features are extracted using fast discrete curvelet transform. These features are applied to hierarchical support vector machine (SVM) classifier to obtain four types of grading levels, namely, normal, mild, moderate and severe. These grading levels are determined based on the number of anomalies such as microaneurysms, hard exudates and haemorrhages that are present in the fundus image. The performance of the proposed system is evaluated using fundus images from the Messidor database. Experiment results show that the proposed system can achieve an accuracy rate of 86.23%.
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