Improving the Curvelet Saliency and Deep Convolutional Neural Networks for Diabetic Retinopathy Classification in Fundus Images

Authors

  • V. T. H. Tuyet Department of Information Systems, Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT) | Vietnam National University-Ho Chi Minh City, Vietnam
  • N. T. Binh Department of Information Systems, Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT) | Vietnam National University-Ho Chi Minh City, Vietnam
  • D. T. Tin Information Systems Engineering Laboratory, Faculty of Electrical and Electronics Engineering, Ho Chi Minh City University of Technology (HCMUT) | Vietnam National University-Ho Chi Minh City, Vietnam
Volume: 12 | Issue: 1 | Pages: 8204-8209 | February 2022 | https://doi.org/10.48084/etasr.4679

Abstract

Retinal vessel images give a wide range of the abnormal pixels of patients. Therefore, classifying the diseases depending on fundus images is a popular approach. This paper proposes a new method to classify diabetic retinopathy in retinal blood vessel images based on curvelet saliency for segmentation. Our approach includes three periods: pre-processing of the quality of input images, calculating the saliency map based on curvelet coefficients, and classifying VGG16. To evaluate the results of the proposed method STARE and HRF datasets are used for testing with the Jaccard Index. The accuracy of the proposed method is about 98.42% and 97.96% with STARE and HRF datasets respectively.

Keywords:

saliency, VGG16, classification, retinal blood vessel, diabetic retinopathy

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How to Cite

[1]
V. T. H. Tuyet, N. T. Binh, and D. T. Tin, “Improving the Curvelet Saliency and Deep Convolutional Neural Networks for Diabetic Retinopathy Classification in Fundus Images”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 1, pp. 8204–8209, Feb. 2022.

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