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Proceedings Paper

Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images
Author(s): J. Anitha; C. Kezi Selva Vijila; D. Jude Hemanth
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Paper Abstract

Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

Paper Details

Date Published: 26 February 2010
PDF: 6 pages
Proc. SPIE 7546, Second International Conference on Digital Image Processing, 754609 (26 February 2010); doi: 10.1117/12.852746
Show Author Affiliations
J. Anitha, Karunya Univ. (India)
C. Kezi Selva Vijila, Karunya Univ. (India)
D. Jude Hemanth, Karunya Univ. (India)


Published in SPIE Proceedings Vol. 7546:
Second International Conference on Digital Image Processing
Kamaruzaman Jusoff; Yi Xie, Editor(s)

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