EARLY DETECTION OF GLAUCOMA USING MODIFIED RESIDUAL U-NET CONVOLUTIONAL NEURAL NETWORK
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Date
2020-12-07Author
Theetharappan, Balasubramaniam
0000-0002-1688-3798
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Glaucoma is the second leading cause of blindness all over the world, with
apparently 75 million cases reported worldwide in 2018. If it’s not diagnosed at
an early stage, glaucoma may cause irreversible damage to the optic nerve
which results in blindness. The Optic head examination is the widely used
structured diagnosis approach in the current medical field for Glaucoma detection
which involves measuring the Optic Cup-to-Disc ratio from the fundus image.
Estimation of Optic Cup-to-Disc requires accurate segmentation of the Optic Cup
and Optic Disc from the fundus which is a tedious and time-consuming task even
for the experienced ophthalmologist. This thesis addresses the challenge by
using the Residual blocks and deep learning segmentation network
(Encode-Decoder Network) to form a model called Modified Residual U-Net
Convolutional Neural Network (Res U-Net) for automatic segmentation of Optic
Cup and Optic Disc. Our experiments include the comparison of various methods
on the publicly available dataset like DRIONS-DB and RIMONE V3. For Optic
Cup and Optic Disc segmentation, my method performs competitively compared
to the other techniques in terms of quality of recognition.