A Novel Approach for Detection of Hard Exudates Using Random Forest Classifier

Diabetic Retinopathy is the major cause of blindness for diabetics in which the retina is damaged. Regular screening system help in detecting the early symptoms like exudates, which are due to the leakage of blood pressure of vessels. The significant role of proposed system is detecting the hard exudates in prevention of visual loss and blindness. Many researchers studied and investigated about detecting the exudates region but not satisfied with their results. Fundamental medical image processing steps with different techniques are implemented by the proposed system. Random Forest is a novel classification which is applied on color retinal images able to classify cluster of data with high accuracy. The performance of the proposed system is obtained by analyzing the accuracy obtained from the Random Forest classifier. These images are obtained from Diabetic Retinopathy Database (DIARETDB) database. The simulation results are obtained with the help of MATLAB 2018. By applying novel classification techniques improves the automatic detection of hard exudates from color retinal images. The achieved accuracy is compared with existing classifiers since the proposed Random Forest classifier provides the accuracy of 99.89% applied on color retinal images.