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In recent years, deep learning is an emerging trend with potential applications in ophthalmology. Glaucoma is one ophthalmic disease where early detection is required to avoid vision loss. In the context of deep learning, convolution neural networks (CNN) are preferred for glaucoma classification because they can extract the highly discriminate features effectively from raw pixel intensities. In our approach, the region-of-interest (ROI) can be segmented from the fundus images using variable mode decomposition (VMD) by splitting the coefficients into 5 recurrence decays and upgrading the normal recurrence ranges. This approach will improve the possibility of identifying even poorly differentiated exudates. Later, a 26-layer CNN which involves six convolution layers, four pooling layers, and one fully connected layer that is designed and trained to extract distinctive features from the selective ROI and classified using …