Support Vector Machine Neural Network Based Optimal Binary Classifier for Diabetic Retinopathy
This paper explores the neural network as optimal binary classifier for diabetic retinopathy. Diabetic retinopathy is an eye syndrome caused by the impediment of diabetes and it can be detected prior for effective treatment. In this investigation, the sets of parameters describing diabetic retinopathy data are taken. In this multi layer neural network and principal component based performance analysis is explored. Selection of the optimal parameters such as number of hidden layers, learning rules and transfer functions are taken into consideration. The classification results are obtained through rigorous experimentation. The vision of patient may start to deteriorate as diabetes progresses and lead to diabetic retinopathy. In this paper to establish diabetic retinopathy, three models like multi layer perception (MLP), Principal Component Analysis (PCA) and Support vector machine(SVM) are explained and their performances are compared. An automated approach for classification of the disease diabetic retinopathy using images is presented. The designed classification structure has about 97 % sensitivity, 99% specifity and correct classification is calculated to be 95.7%. Testing grades were found to be complaint with the accepted results that are imitative from the physician’s direct diagnosis. Result shows that this new neural network SVM model is more accurate than the other NN models. These results suggest that this model is effective for classification of Diabetic Retinopathy.
Gauri Borkhade, Dr. Ranjana Raut