Journal of Medical Systems

, 42:247 | Cite as

Diabetic Retinopathy Diagnosis from Retinal Images Using Modified Hopfield Neural Network

  • D. Jude Hemanth
  • J. Anitha
  • Le Hoang SonEmail author
  • Mamta Mittal
Image & Signal Processing
Part of the following topical collections:
  1. Image & Signal Processing


Disease diagnosis from medical images has become increasingly important in medical science. Abnormality identification in retinal images has become a challenging task in medical science. Effective machine learning and soft computing methods should be used to facilitate Diabetic Retinopathy Diagnosis from Retinal Images. Artificial Neural Networks are widely preferred for Diabetic Retinopathy Diagnosis from Retinal Images. It was observed that the conventional neural networks especially the Hopfield Neural Network (HNN) may be inaccurate due to the weight values are not adjusted in the training process. This paper presents a new Modified Hopfield Neural Network (MHNN) for abnormality classification from human retinal images. It relies on the idea that both weight values and output values can be adjusted simultaneously. The novelty of the proposed method lies in the training algorithm. In conventional method, the weights remain fixed but the weights are changing in the proposed method. Experimental performed on the Lotus Eye Care Hospital containing 540 images collected showed that the proposed MHNN yields an average sensitivity and specificity of 0.99 and accuracy of 99.25%. The proposed MHNN is better than HNN and other neural network approaches for Diabetic Retinopathy Diagnosis from Retinal Images.


Hopfield neural network Retinal images Disease diagnosis Classification accuracy 



This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.05-2018.02.

Compliance with ethical standards

The authors declare that they do not have any conflict of interests. This research does not involve any human or animal participation. All authors have checked and agreed with the submission.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of ECEKarunya UniversityCoimbatoreIndia
  2. 2.VNU Information Technology InstituteVietnam National UniversityHanoiVietnam
  3. 3.G.B. Pant Govt. Engineering CollegeNew DelhiIndia

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