A Comparative Study of Convolutional Neural Network and Twin SVM for Automatic Glaucoma Diagnosis

The number of people who suffer from tension problem is growing around the world, which is capable of causing other very serious illness like glaucoma. This last is a chronic and irreversible disease which can lead to vision loss and blindness, that's why doctors recommend continuous follow-up all the time. The development of computer aided diagnosis systems (CAD) in the medical field has been viewed with great interest by doctors to have a second diagnosis. In our previous study concerning classical methods, a Twin Support Vector Machine (TWSVM) classifier was proposed to glaucoma images classification by using handcrafted features. To overcome the main limits of classical approach, we explore a deep learning algorithm based on convolutional neural network (CNN) for the automatic generation of features depending on any used dataset. To highlight the impact of the CNN classifier in medical diagnosis, several empirical studies was performed based on convolution layers numbers and activation function. Obtained results will be compared and discussed with previous work based on TWSVM, by applying the RimOne dataset.