SVM and Neural Network based Diagnosis of Diabetic Retinopathy

Abstract

Diabetic retinopathy (DR) is an eye disease caused by the complication of diabetes and we should detect it early for effective treatment. As diabetes progresses, the vision of a patient may start deteriorate and lead to diabetic retinopathy. As a result, two groups were identified, namely nonproliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). In this paper, to diagnose diabetic retinopathy, two models like Probabilistic Neural network (PNN) and Support vector machine (SVM) are described and their performances are compared. Experimental results show that PNN has an accuracy of 89.60% and SVM has an accuracy of 97.608 %. This infers that the SVM model outperforms the other model.

Real-Data Comparison of Data Mining Methods in Prediction of Diabetes in Iran

“…In a study to compare NN and SVM for diagnosing diabetic retinopathy, Priya and Aruna [15] reported better performance for SVM (accuracy 89.6% for NN and 97.61% for SVM). In a study comparing three data mining methods (NN, SVM, and decision tree) with LR, Kim et al [10] concluded that the decision tree algorithm slightly outperformed (AUC, 0.892) the other data mining techniques, followed by the artificial neural network (AUC, 0.874) and SVM (AUC, 0.876), which is in contradiction to our results.…”

“…Yu et al [9] compared SVM and LR for the classification of undiagnosed diabetes or pre-diabetes vs. no diabetes. Priya and Aruna [15] compared NN and SVM for the diagnosis of diabetic retinopathy.…”

Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening

“…In addition, there are some developments on automatic systems that provide a more detailed classification of diabetic retinopathy stages namely, normal, nonproliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) [31][32][33]56]. Some developed systems focus on the detection of diabetic retinopathy features, such as microaneurysms, exudates, hemorrhages and others.…”

“…The proposed system uses colour fundus images, where the features are extracted from the raw image by using various image processing techniques and fed into a Support Vector Machine (SVM) for classification. The system has been later enhanced by combining two types of classifiers, a Probabilistic Neural Network (PNN) and a Support Vector Machine [32].…”

Investigation of the severity level of diabetic retinopathy using supervised classifier algorithms

“…Quellec et al [16] generated an optimal set of filters to distinguish the lesions. Priya et al [17] proposed a method to diagnose DR, and the performances of SVM and PNN were analysed. The region of interest (ROI) was segmented using the FCM means algorithm.…”