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

      [1] Silink GRM, Tuomilehto J, Mbanya JC, Venkat Narayan KM & Judy F (2010), Research priorities: Prevention and control of diabetes with a focus on low and middle income countries. World Health Organization.

      [2] Rocha A, Carvalho T, Jelinek HF, Goldenstein S & Wainer J (2012), Points of interest and visual dictionaries for automatic retinal lesion detection. IEEE Transactions on Biomedical Engineering 59, 2244–2253.

      [3] Eswaran C, Mukti FA & Hashim N (2014), Comparison of classifiers for retinal pathology images using SURF and Bag-of- Words Model. Proceedings of the International Conference on Artificial Intelligence and Pattern Recognition, pp. 72–78.

      [4] Nor FN, Ang EL, Bavaanandan NG, Loong AM, Mohamad AS, Maziah I, Mohd. AMY, Nor AI, Nik MKNS, Roslin AAA & Shelina OM (2011), Clinical practice guidelines-Screening of diabetic retinopathy.

      [5] Candes E, Demanet L, Donoho D & Ying L (2006), Fast discrete curvelet transforms. Multiscale Modeling and Simulation 26, 861-899.

      [6] Burges CJ (1998), A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121-167.

      [7] Akram MU, Tariq A, Anjum MA & Javed MY (2012), Automated detection of exudates in colored retinal images for diagnosis of diabetic retinopathy. Applied Optics 51, 4858-4866.

      [8] Tjandrasa H, Putra RE, Wijaya AY & Arieshanti I (2013), Classification of non-proliferative diabetic retinopathy based on hard exudates using soft margin SVM. Proceedings of the IEEE International Conference on Control System, Computing and Engineering, pp. 376-380.

      [9] Akram MU, Tariq A, Khan SA & Javed MY (2014), Automated detection of exudates and macula for grading of diabetic macular edema. Computer Methods and Programs in Biomedicine 114, 141-152.

      [10] Esmaeili M, Rabbani H, Dehnavi AM & Dehghani A (2010), A new curvelet transform based method for extraction of red lesions in digital color retinal images. Proceedings of the 17th IEEE International Conference on Image Processing, pp. 4093-4096.

      [11] ADCIS SA (2017), Methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology (MESSIDOR).




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

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