Diabetic Retinopathy Diagnosis from Retinal Images Using Modified Hopfield Neural Network
- 78 Downloads
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.
KeywordsHopfield 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.
- 1.Zhou, C., Chase, J. G., Ismail, H., Signal, M. K., Haggers, M., Rodgers, G. W., and Pretty, C., Silicone phantom validation of breast cancer tumor detection using nominal stiffness identification in digital imaging elasto-tomography (DIET). Biomedical Signal Processing and Control 39:435–447, 2018.CrossRefGoogle Scholar
- 7.Luculescu, M. C., and Lache, S., Computer-aided diagnosis system for retinal diseases in medical imaging. WSEAS Trans Syst 7:264–276, 2008.Google Scholar
- 11.Shaeidi, A., An algorithm for identification of retinal microaneurysms. J Serbian Soc Comput Mech 4:43–51, 2010.Google Scholar
- 13.Lim, G., Lee, M. L., Hsu, W., & Wong, T. Y., Transformed Representations for Convolutional Neural Networks in Diabetic Retinopathy Screening. In: AAAI Workshop: Modern Artificial Intelligence for Health Analytics, pp. 21–25, 2014.Google Scholar
- 17.Perova, I., and Bodyanskiy, Y., Fast medical diagnostics using autoassociative neuro-fuzzy memory. International Journal of Computing 16(1):34–40, 2017.Google Scholar
- 18.Rajasekaran, S., and Pai, G. V., Neural Networks, Fuzzy Systems and Evolutionary Algorithms:Synthesis and Applications. PHI Learning Pvt. Ltd. 2nd edition. Delhi, India, 2017.Google Scholar
- 21.Jha, S., Kumar, R., Chatterjee, J. M., Khari, M., Yadav, N., and Smarandache, F., Neutrosophic softset decision making for stock trending analysis. Evol. Syst., 1–7, 2018. https://doi.org/10.1007/s12530-018-9247-7.
- 22.Dey, A., Broumi, S., Bakali, A., Talea, M., and Smarandache, F., A new algorithm for finding minimum spanning trees with undirected neutrosophic graphs. Granular Computing, 1–7, 2018. https://doi.org/10.1007/s41066-018-0084-7.
- 25.Nguyen, G. N., Ashour, A. S., and Dey, N., A survey of the state-of-the-arts on neutrosophic sets inbiomedical diagnoses. Int. J. Mach. Learn. Cybern., 1–13, 2017. https://doi.org/10.1007/s13042-017-0691-7.
- 27.Thanh, N. D., Son, LH, and Ali, M., Neutrosophic recommender system for medical diagnosis based onalgebraic similarity measure and clustering. In: Fuzzy Systems (FUZZ-IEEE), 2017 IEEE International Conference on (pp. 1–6). IEEE, 2017.Google Scholar
- 33.Tanthuwapathom, R., and Hnoohom, N., Detection of Diabetic Retinopathy Using ImageProcessing. In: International Symposium on Natural Language Processing, pp. 259–265: Springer, Cham, 2016.Google Scholar