Severity classification of non-proliferative diabetic retinopathy using convolutional support vector machine

Diabetic retinopathy is the principle disease that can make blindness. The early stage of diabetic retinopathy is Non-Proliferative Diabetic Retinopathy, which is splitted into three levels namely mild, moderate, and severe. This research is conducted to classify data on Base21, Base 13, and Base 12 from the Messidor database into 2 classes (mild, severe) and 3 classes (mild, moderate, severe). This research is useful for minimizing the funds spent and can be a breakthrough for people who has diabetic retinopathy that lack the hospital diagnosing funds. There are five stages in this research, those are pre-processing, image enhancement, feature extraction, feature reduction, and classification. The pre-processing step consists of cropping and resizing data, then the image is enhanced using Contrast Limited Adaptive Histogram Equalization, Morphology Contrast Enhancement, and Homomorphic. The results of the image enhancement are used as the inputs to the feature extraction layer which in this study uses the GoogLeNet, ResNet18, ResNet50, and ResNet101. Then the data is reduced using the Principle Component Analysis and Relief before entering the classification layer. The Support Vector Machine-Naive Bayes is used to replace the fully connected layer in Convolutional Neural Network to speed up and to optimize the classification process. The best results from the experiments are obtained by the Homomorphic, ResNet50, and Relief before entering to the Support Vector Machine-Naive Bayes. The Homomorphic obtains 85.87% accuracy, ResNet50 can achieve 86.76% accuracy, and the Relief can reach 89.12% accuracy.