Open AccessAn automated grading system for diabetic retinopathy using curvelet transform and hierarchical classification
Author(s) -
Fanji Ari Mukti,
C. Eswaran,
Noramiza Hashim,
Chiung Ching Ho,
Mohamed Uvaze Ahamed Ayoobkhan
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.15.11375
Subject(s) - artificial intelligence , grading (engineering) , diabetic retinopathy , support vector machine , fundus (uterus) , pattern recognition (psychology) , computer science , curvelet , computer vision , classifier (uml) , medicine , ophthalmology , wavelet transform , diabetes mellitus , engineering , wavelet , civil engineering , endocrinology
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%.