Multi-Layer Perceptron and Radial Basis Function Neural Network Models for Classification of Diabetic Retinopathy Disease Using Video-Oculography Signals
Diabetes Mellitus (Diabetes) is a disease based on insulin hormone disorders and causes high blood glucose. Clinical findings determine that diabetes can be diagnosed by electrophysiological signals obtained from the vital organs. 'Diabetic Retinopathy' is one of the most common eye diseases resulting on diabetes and it is the leading cause of vision loss due to structural alteration of the retinal layer vessels. In this study, features of horizontal and vertical Video-Oculography (VOG) signals have been used to classify non-proliferative and proliferative diabetic retinopathy disease. Twenty-five features are acquired by using discrete wavelet transform with VOG signals which are taken from 21 subjects. Two models, based on multi-layer perceptron and radial basis function, are recommended in the diagnosis of Diabetic Retinopathy. The proposed models also can detect level of the disease. We show comparative classification performance of the proposed models. Our results show that proposed the RBF model (100%) results in better classification performance than the MLP model (94%).
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132671Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1072
 Vallabha, D., Dorairaj, R., Namuduri, K. and Thompson, H., “Automated Detection and Classification of Vascular Abnormalities in Diabetic Retinopathy”, Proceedings of 13th IEEE Signals, Systems and Computers, 1625–1629, 2004.
 Walter, T., Klein, J. C., Massin, P. And Erginay, A., “A Contribution of Image Processing to The Diagnosis of Diabetic Retinopathy-Detection of Exudates in Colour Fundus Images of The Human Retina”, IEEE Transactions on Medical Imaging, 22(10): 1236-1243, 2002.
 Priya, R. and Aruna, P., “Diagnosis of Diabetic Retinopathy Using Machine Learning Techniques”, ICTACT Journal on Soft Computing, 3(4): 563-575, 2013.
 Rajput, Y. M., Manza, R. R., Patwari, M. B., Rathod, D. D., Borde, P. L. and YML Pawar, P. L., “Detection of Non-Proliferative Diabetic Retinopathy Lesions Using Wavelet and Classification Using K-Means Clustering”, International Conference on Communication Networks (ICCN), 381-387, 2015.
 Noronha, K., Acharya, U. R., Nayak, K. P., Kamath, S. and Bhandary, S. V., “Decision Support System for Diabetic Retinopathy Using Discrete Wavelet Transform”, Journal of Engineering in Medicine, 227(3): 251-261, 2012.
 Gürkan, G., Gürkan, S. ve Uşaklı, A. B., “EOG Sinyalleri İçin Sınıflandırma Algoritmalarının Karşılaştırılması”, 20. IEEE Sinyal İşleme ve İletişim Uygulamaları Kurultayı (SIU), 1-4, 2012.
 Banerjee, A., Datta, S., Pal, M., Konar, A., Tibarewala, D. N. and Janarthanan, R., “Classifying Electrooculogram to Detect Directional Eye Movements”, Int. Conf. on Comp. Intellig.: Model. Tech. and App. (CIMTA), 10: 67-75, 2013.
 Lawrence, Y., Chun-Liang, H., Tzu-Ching, L., Jui-Sen, T. and Shih- Ming, C., “EOG-Based Human–Computer Interface System Development”, Expert Systems with Applications, 37:3337-3343, 2009.
 Kim, O., Doh, N. L., Youm, Y. and Chung, W. K., “Robust Discrimination Method of The Electrooculogram Signals for Human-Computer Interaction Controlling Mobile Robot”, Intelligent Automation& Soft Computing, 13:319-336, 2013.
 Goswami, J. C. and Chan, A. K., “Fundamentals of Wavelets”, Second Edition, John Wiley&Sons, Inc., 2011.
 Kaya, C., Erkaymaz, O., Ayar, O. ve Özer, M., "Ayrık Dalgacık Dönüşümü Kullanarak VideoOkülografi (VOG) Sinyallerinden Diyabetik Retinopati Hastalığının Fizyolojik Etkilerinin Belirlenmesi", Tıp Teknolojileri Ulusal Kongresi (TIPTEKNO), 142-145, 2016.
 Subaşı, A., “EEG Signal Classification Using Wavelet Feature Extraction and A Mixture of Expert Model”, Expert Systems with Applications, 32: 1084-1093, 2007.
 Kaya, C., Erkaymaz, O., Ayar, O. ve Özer, M., "Video-Okülografi (VOG) Sinyallerinden Diyabetik Retinopati Hastalığının Yapay Sinir Ağları İle Tespiti",25. IEEE Sinyal İşleme ve İletişim Uygulamaları Kurultayı (SIU), 1-4, 2017.
 Erkaymaz, H., Özer, M. ve Orak, İ. M., “Detection of Directional Eye Movements Based on The Electrooculogram Signals through an Artificial Neural Network”, Chaos Solitons&Fractals, 56: 202-208, 2015.
 T. Xie, H. Yu and B. Wilamowski, "Comparison between Traditional Neural Networks and Radial Basis Function Networks," IEEE International Symposium on Industrial Electronics, 1194-1199, 2011.