Computer-Aided Diagnosis of Glaucoma at Early-Stage Using Retinal Fundus Images

Detection of glaucoma in the initial condition is necessary to protect vision mislaying through appropriate medical care. The available clinical instruments are nonautomated and work on manual examinations. In this article, we proposed a computer-aided diagnostics system for early-stage glaucoma detection. This study has applied a 2-dimensional variational mode decomposition (2D-VMD) tool for fundus photo analysis. Then, Haralick texture descriptors have been computed from the analyzed, processed images. The Students t-test approach is utilized for the chosen robust features. In the end, a support vector machine (SVM) classifier has been used for glaucoma screening. The effectiveness of the introduced method is tested using a publically available dataset. The proposed framework can effectively be used to detect mild/early glaucoma with correctness of 91.66%.