New computer aided diagnosis system for glaucoma disease based on twin support vector machine

Glaucoma is a group of eye diseases that cause an irreparable decrease in the view field which is the second most common and leading causes of blindness among retinal diseases. Computer aided diagnosis (CAD) system is an emerging field in medical informatics which has high importance for providing prognosis of diseases. Research efforts have reported with increasing confirmation that the twin support vector machines (TWSVM) have greater accurate diagnosis ability. The goal of TWSVM is to construct two non-parallel planes for each class such that each hyper-plane is closer to one of two classes and as far as possible from the other one. In this paper, we propose a new CAD system for glaucoma diagnosis using TWSVM and three heterogeneous families of feature extraction. In this work, we have used 169 images to classify into normal and glaucoma classes. The performance of the method is evaluated using classification accuracy, sensitivity, specificity and receiver operating characteristic (ROC) curves. The results show that the highest classification accuracy (98,53%) is obtained for the TWSVM using Gaussian kernel function, and this is very promising compared to the SVM results.