Automated glaucoma diagnosis using deep learning approach

Glaucoma is one of the common causes of blindness worldwide. It leads to deterioration in vision and quality of life if it is not cured early. This paper addresses the feasibility of developing an automatic feature learning technique for detecting glaucoma in colored retinal fundus images using a deep learning method. A fully automated system based on convolutional neural network (CNN) is developed to distinguish between normal and glaucomatous patterns for diagnostic decisions. Unlike traditional methods where the optic disc features are handcrafted, the features are extracted automatically from the raw images by CNN and fed to the SVM classifier to classify the images into normal or abnormal. We demonstrate an accuracy, specificity and sensitivity of 88.2%, 90.8%, and 85%, respectively which compared favorably to the-state-of-the-art but at considerably lower computational cost. The obtained preliminary results clearly demonstrate that the proposed deep learning method is promising in automatic diagnosis of glaucoma.