An automated grading system for diabetic retinopathy using curvelet transform and hierarchical classification

 
 
 
  • Abstract
  • Keywords
  • References
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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%.

     


  • Keywords


    Automated screening system; Curvelet transform; Diabetic retinopathy; Fundus image; SVM classifier.

  • References


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Article ID: 11375
 
DOI: 10.14419/ijet.v7i2.15.11375




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