89
Views
6
CrossRef citations to date
0
Altmetric
Articles

Diabetic Retinopathy Diagnosis in Retinal Images Using Hopfield Neural Network

, &

REFERENCES

  • Z. Yingfing, H. Mingguang, and C. Nathan, “The worldwide epidemic of diabetic retinopathy,” Indian J. Ophthalmol., Vol. 60, pp. 428–31, 2012.
  • S. Sivaprasad, B. Gupta, R. Crosby-Nwaobi, and J. Evans, “Prevalence of diabetic retinopathy in various ethnic groups: a worldwide perspective,” Surv. Opthalmol., Vol. 57, pp. 347–70, 2012.
  • J. Anitha, C. K. Vijila, A. I. Selvakumar, et al., “Automated multi-level pathology identification techniques for abnormal retinal images using artificial neural networks,” Brit. J. Ophthamol., Vol. 96, pp. 220–3, 2012.
  • J. Jiang, P. Trundle, and J. Ren, “Medical image analysis with artificial neural networks,” Comput. Med. Imag Graph., Vol. 34, pp. 617–31, 2010.
  • N. B. Prakash, D. Selavthi, and G. R. Hemalakshmi, “Development of algorithm for dual stage classification to estimate severity level of diabetic retinopathy in retinal images using soft computing techniques,” Int. J. Electr Eng. Inform., Vol. 6, pp. 717–39, 2014.
  • M. Baroni, P. Fortunato, L. Pollazzi, and A. La Torre, “Multiscale Filtering and Neural Network Classification for Segmentation and Analysis of Retinal Vessels,” Webmed Central Biomed. Eng., Vol. 3, p. WMC003588, 2012, doi: 10.9754/journal.wmc.2012.003588
  • D. Selvathi, N. B. Prakash, and B. Neethi, “Automated detection of diabetic retinopathy for early diagnosis using feature extraction and support vector machines,” Int. J. Emerg. Technol. Adv. Eng., Vol. 2, pp. 103–8, 2012.
  • B. Gauri, and R. Raut, “Support vector machine neural network based optimal binary classifier for diabetic retinopathy,” Int. J. Innov. Res. Comput. Commun. Eng., Vol. 3, pp. 136–41, 2015.
  • S. Manoj, Muralidharan, and P. M. Sandeep, “Neural network based classifier for retinal blood vessel segmentation,” Int. J. Recent Trends Electr. Electron. Eng., Vol. 3, pp. 44–53, 2013.
  • A. S. Jadhav, and B. P. Pushpa, “Classification of diabetes retina images using blood vessel area,” Int. J. Cybern. Inform., Vol. 4, pp. 251–7, 2015.
  • K. A. Nithya, and A. Rajini, “Classification of normal and abnormal retinal images using neural networks,” Int. J. Adv. Res. Comput. Eng. Technol., Vol. 3, pp. 3111–5, 2014.
  • C. P. R. Chand, and J. Dheeba, “Automatic detection of exudates in colour fundus retinopathy images,” Indian J. Sci. Technol., Vol. 8, pp. 1–6, 2014.
  • S. S. Raja, and S. Vasuki, “Screening diabetic retinopathy in developing countries using retinal images,” Appl. Med. Inform., Vol. 36, pp. 13–22, 2015.
  • C. Aravind, M. Ponnibala, and S. Vijayachitra, “Automatic detection of microaneurysms and classification of diabetic retinopathy images using SVM technique,” Int. J. Comput. Appl., Vol. 11, pp. 18–22, 2013.
  • M. S. Gabor, T. Erika, L. Lenke. et al., “Automated classifiers for early detection and diagnosis of retinopathy in diabetic eyes,” BMC Bioinformatics., Vol. 15, pp. 106–12, 2014.
  • H. Tjandrasa, I. Arieshanti, and R. Anggoro, “Classification of non proliferative diabetic retinopathy based on segmented exudates using k-means clustering,” Int. J. Image Graph. Signal Proc., Vol. 7, pp. 1–8, 2014.
  • T. A. Ahmad, and E. B. Valentina, “Classification and detection of diabetic retinopathy,” Adv. Intell. Anal. Med. Data Decis. Support Syst., Vol. 473, pp. 135–45, 2013.
  • M. Ponnibala, and S. Vijayachitra, “A sequential learning method for detection and classification of exudates in retinal images to assess diabetic retinopathy,” J. Biol. Syst., Vol. 22, pp. 413–21, 2014.
  • R. Pires, “Advancing Bag-of-Visual-Words representations for lesion classification in retinal images,” PLoS ONE., Vol. 9, pp. E96814, 2014.
  • D. Marin, A. Aquino, M. E. Gegundez-Arias, et al., “A new supervised method for blood vessel segmentation in retinal images by using gray level and moment invariant based features,” IEEE Trans. Med. Imag., Vol. 30, pp. 146–58, 2011.
  • S. Chris, and B. Toby, Fundamentals of Digital Image Processing. Chichester: Wiley Blackwell Publishers, 2011.
  • D. J. Hemanth, and J. Anitha, “Modified cross-over techniques in Genetic Algorithm for performance enhancement of retinal image classification system,” Proceedings of 3rd International Conference on Computational Intelligence and Information Technology, 2013, pp. 169–74.
  • Matlab, User's Guide. Natick, MA: The Math Works, Inc., 1994–2002.
  • D. J. Hemanth, and J. Anitha, “Performance enhanced hybrid artificial neural network for abnormal retinal image classification,” Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications. Advances in Intelligent Systems and Computing, Vol. 201, 2012, pp. 367–78.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.