Abstract
Optical field sensitivity test results are essential for accurate and efficient diagnosis of blinding diseases. The classification of eye diseases in retinal images is the focus of several researches in the field of medical image processing. Diabetic retinopathy is the disease caused by disorder of diabetes. The vision of patient commences to weaken as diabetes grow and leads to retinopathy; prior detection is must for effective treatment. Multiple detection techniques survey for eye diseases and play a vital role as screening tool. Anomaly of retina due to diabetic is detected through numerous techniques. As optimal binary classifier, artificial neural network is proposed in this paper. The sets of constraints which elaborate EEG eye states in database are covered in this investigation. Indeed, performances are classified as normal and diseased. Artificial neural networks are often used as powerful and intelligent classifier for early detection and accurate diagnosis of the diseases. Thus, the result concludes that the support vector machine (SVM) model is operational for classification of eye states with total accuracy of 90%.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Parsaei, H., Moradi, H.M.: Development and verification of artificial neural network classifiers for eye diseases diagnosis. ICBME (2008)
Kumar, H.P., Venugopal, H.: Diagnosis of glaucoma using artificial neural networks. Int. J. Comput. Appl. 180(30), 29–31 (2018)
Sheeba, O., George, J.: Glaucoma detection using artificial neural network. IACSIT Int. J. Eng. Tech. 6(5), 158–161 (2014)
Povilas, T., Saltenis, V.: Neural network as an ophthalmologic disease classifier. Inf. Technol. Control 36, 365–371 (2007)
Borkhade, G., Raut, R.: Application of neural network for diagnosing eye disease. Int. J. Electron. Commun. Soft Comput. Sci. Eng. 174–176 (2015)
Osareh, A., Mirmehdi, M., Thomas, B., Markham, R.: Comparative exudates classification using support vector machine and neural networks. In: Medical Image Computing and Computer Assisted Intervention. Lecture Notes in Computer Science, Vol. 2489, pp. 413–420 (2002)
Guven, A., Kara, S.: Diagnosis of the macular diseases from pattern electroretinography signals using artificial neural networks. Expert Syst. Appl. 361–366 (2006)
Vallabha, D., Dorairaj, R., Namuduri, K.R., Thompson, H.: Automated detection and classification of vascular abnormalities in diabetic retinopathy. In: Asilomar Conference on Signals, Systems and Computers, vol. 38, no. 2, pp. 1625–1629 (2001)
Hitz, W., Reitsamer, H.A.: Application of discriminant, classification tree and neural network analysis to differentiate between potential Glaucoma suspects with and without visual field defects. J. Theor. Med. 5(3), 161–170 (2003)
Abdel-Haleim, A., Youssif, A.-R., Ghalwash, A.Z., Sabry, A.A., Ghoneim, A.-R.: Optic disc detection from normalized digital fundus images by means of a vessels direction matched filter. IEEE Trans. Med. Imag. 27(1), 11–18 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Borkhade, G., Raut, R. (2020). Neural Network Classifier for Diagnosis of Diabetic Retinopathy. In: Zhang, YD., Mandal, J., So-In, C., Thakur, N. (eds) Smart Trends in Computing and Communications. Smart Innovation, Systems and Technologies, vol 165. Springer, Singapore. https://doi.org/10.1007/978-981-15-0077-0_9
Download citation
DOI: https://doi.org/10.1007/978-981-15-0077-0_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0076-3
Online ISBN: 978-981-15-0077-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)