Open Access

ARTICLE

Comparison of Detection and Classification of Hard Exudates Using Artificial Neural System vs. SVM Radial Basis Function in Diabetic Retinopathy

by V. Sudha^1,*, T. R. Ganesh Babu², N. Vikram¹, R. Raja²

1 Sona College of Technology, Salem, 636201, India
2 Muthayammal Engineering College, Rasipuram, 637408, India

* Corresponding Author: V. Sudha. Email:

Molecular & Cellular Biomechanics 2021, 18(3), 139-145. https://doi.org/10.32604/mcb.2021.016056

Received 02 February 2021; Accepted 19 March 2021; Issue published 15 July 2021

Abstract

Diabetic Retinopathy (DR) is a disease that occurs in the eye which results in blindness as it passes to proliferative stage. Diabetes can significantly result in symptoms like blurring of vision, kidney failure, nervous damage. Hence it has become necessary to identify retinal damage that occurs in diabetic eye due to raised glucose level in its initial stage itself. Hence automated detection of anamoly has become very essential. The appearance of crimson and yellow lesions is considered as the earliest symptoms of DR which are called as hemorrhages and exudates. If DR is analysed at initial stage, blindness does not occur. The damage in retina can hinder the light that passes through nerves of the eye leading to visual loss. The motivation behind this research is to reduce the number of false positives by accurate detection which is possible using proposed fuzzy system based on ANN. Though several classifiers are available to detect the exudates this paper makes analysis of support vector machine using radial basis kernel function with proposed ANN technique. Also, adaptive neuro fuzzy inference system segmentation is performed after feature extraction technique, which makes classifer to outperform. The evaluation results showed that proposed artificial neural network based on fuzzy approach attained significant results compared to other classifiers. Moreover, the proposed algorithm has significant accuracy of 94% and minimum error rate has been observed.

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