An hybrid feature space from texture information and transfer learning for glaucoma classification

Abstract Glaucoma is a progressive eye disease due to the increase in intraocular pressure. Accurate early detection may prevent vision loss. Most algorithms in the literature are not feasible for use in screening programs since they are not able to handle a wide diversity of images. We conducted an extensive study to determine the best set of features for image representation. Our feature extraction methodology included the following descriptors: LBP, GLCM, HOG, Tamura, GLRLM, morphology, and seven CNN architectures, that results in 30.682 features. Then, we used the gain ratio to order the features by importance and select the best set for glaucoma classification. Our tests were performed using 1675 images of DRISHTI, RIM-ONE, HRF, JSIEC, and ACRIMA databases. We concluded that a combination of the GLCM and pre-trained CNN’s has the potential to be used in a computer aid system for glaucoma detection. Our approach achieved an accuracy of 93.61%.