Citation

Ari Mukti, Fanji and Eswaran, Chikkannan and Hashim, Noramiza and Ho, Chiung Ching and Ahamed Ayoobkhan, Mohamed Uvaze (2018) An automated grading system for diabetic retinopathy using curvelet transform and hierarchical classification. International Journal of Engineering & Technology, 7 (2.15). p. 154. ISSN 2227-524X

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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%.

Item Type:	Article
Uncontrolled Keywords:	Machine learning, Automated screening system, Curvelet transform, Diabetic retinopathy, Fundus image, SVM classifier, Support vector machines
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	11 Nov 2020 14:07
Last Modified:	11 Nov 2020 14:07
URII:	http://shdl.mmu.edu.my/id/eprint/7341

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