Detection of the Emergence of Exudate on the Image of Retina Using Extreme Learning Machine Method

Diabetic retinopathy is a health problem that cause damage to the retinal blood vessels and occurs in more than half of people who suffer from diabetes. It is estimated that around 28 million people experience loss of sight for this reason. Thus, the system for detecting early signs of diabetic retinopathy will be very helpful and one of first signs of the onset of symptoms of diabetic retinopathy is the appearance of exudates in the retinal image of the eye. To build an exudate emergence detection system, in this study use the method of extreme learning machine (ELM) which has a fast learning speed. This system uses the gray level co-occurrence matrix feature extraction with 6 features, namely contrast, homogeneity, correlation, ASM, energy and dissimilarity. To get the best model, six scenarios are used by distinguishing the pre-processing flow. The pre processing stage carried out by all scenarios is optic disc removal, green channel separation, contrast limited adaptive histogram equalization (CLAHE) followed by two different preprocessing lines, namely applying brightness and dilation and erosion operations. Then the second path is radon transform, top-hat filtering, discrete wavelet transform and dilation and erosion. The best model results reached the best accuracy value of 65% with a combination of multiquadric activation functions and 30 hidden neurons.