Automatic Diagnosis of Glaucoma using Ensemble based Deep Learning Model

In both developing and developed nations, glaucoma is the primary cause of vision loss. Identifying and classifying glaucoma in the early stage will provide the patients with sufficient care and an effective way to support the eye surgeon. This is the opinion of an initiative to identify and classify glaucoma early with the use of a comprehensive learning system using fundus images. Three pre-trained convolution neural network (ConvNet) architectures are being used for the classification of glaucoma in the proposed framework: the Residual Network (ResNet), the Visual geometry group network (VGGNet), and GoogLeNet. The tests are performed in private and standard benchmark data sets to verify the performance of the proposed system. In terms of accuracy, precision, specificity, sensitivity, and F1, the proposed algorithm is compared to three various ConvNets. The findings obtained are encouraging, and the dominance in performance measurements to detect and diagnose glaucoma using fundus images in the proposed ensemble of deep learning architectures will be verified.