Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

26 February 2010 Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

J. Anitha, C. Kezi Selva Vijila, D. Jude Hemanth

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Proceedings Volume 7546, Second International Conference on Digital Image Processing; 754609 (2010) https://doi.org/10.1117/12.852746
Event: Second International Conference on Digital Image Processing, 2010, Singapore, Singapore

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.

Citation Download Citation

J. Anitha, C. Kezi Selva Vijila, and D. Jude Hemanth "Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images", Proc. SPIE 7546, Second International Conference on Digital Image Processing, 754609 (26 February 2010); https://doi.org/10.1117/12.852746

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