Glaucoma is an eye disease that causes damage to the optic nerve, which can
lead to visual loss and permanent blindness. Early glaucoma detection is
therefore critical in order to avoid permanent blindness. The estimation of the
cup-to-disc ratio (CDR) during an examination of the optical disc (OD) is used
for the diagnosis of glaucoma. In this paper, we present the EDDense-Net
segmentation network for the joint segmentation of OC and OD. The encoder and
decoder in this network are made up of dense blocks with a grouped
convolutional layer in each block, allowing the network to acquire and convey
spatial information from the image while simultaneously reducing the network's
complexity. To reduce spatial information loss, the optimal number of filters
in all convolution layers were utilised. In semantic segmentation, dice pixel
classification is employed in the decoder to alleviate the problem of class
imbalance. The proposed network was evaluated on two publicly available
datasets where it outperformed existing state-of-the-art methods in terms of
accuracy and efficiency. For the diagnosis and analysis of glaucoma, this
method can be used as a second opinion system to assist medical
ophthalmologists