  Glaucoma is a severe eye disease that is known to deteriorate optic never
fibers, causing cup size to increase, which could result in permanent loss of
vision. Glaucoma is the second leading cause of blindness after cataract, but
glaucoma being more dangerous as it is not curable. Early diagnoses and
treatment of glaucoma can help to slow the progression of glaucoma and its
damages. For the detection of glaucoma, the Cup to Disc ratio (CDR) provides
significant information. The CDR depends heavily on the accurate segmentation
of cup and disc regions. In this paper, we have proposed a modified M-Net with
bidirectional convolution long short-term memory (LSTM), based on joint cup and
disc segmentation. The proposed network combines features of encoder and
decoder, with bidirectional LSTM. Our proposed model segments cup and disc
regions based on which the abnormalities in cup to disc ratio can be observed.
The proposed model is tested on REFUGE2 data, where our model achieves a dice
score of 0.92 for optic disc and an accuracy of 98.99% in segmenting cup and
disc regions
