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Glaucoma is a severe eye disease that is known to deteriorate optic neverfibers, causing cup size to increase, which could result in permanent loss ofvision. Glaucoma is the second leading cause of blindness after cataract, butglaucoma being more dangerous as it is not curable. Early diagnoses andtreatment of glaucoma can help to slow the progression of glaucoma and itsdamages. For the detection of glaucoma, the Cup to Disc ratio (CDR) providessignificant information. The CDR depends heavily on the accurate segmentationof cup and disc regions. In this paper, we have proposed a modified M-Net withbidirectional convolution long short-term memory (LSTM), based on joint cup anddisc segmentation. The proposed network combines features of encoder anddecoder, with bidirectional LSTM. Our proposed model segments cup and discregions 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 dicescore of 0.92 for optic disc and an accuracy of 98.99% in segmenting cup anddisc regions

M-Net with Bidirectional ConvLSTM for Cup and Disc Segmentation in Fundus Images