IDR Logo

Please use this identifier to cite or link to this item:
Title: Deep convolutional neural network-based patch classification for retinal nerve fiber layer defect detection in early glaucoma
Authors: Panda R.
Puhan N.B.
Rao A.
Mandal B.
Padhy D.
Panda G.
Keywords: convolution neural network
deep learning
fundus image
patch classification
retinal nerve fiber layer
Issue Date: 2018
Abstract: Glaucoma is a progressive optic neuropathy characterized by peripheral visual field loss, which is caused by degeneration of retinal nerve fibers. The peripheral vision loss due to glaucoma is asymptomatic. If not detected and treated at an early stage, it leads to complete blindness, which is irreversible in nature. The retinal nerve fiber layer defect (RNFLD) provides an earliest objective evidence of glaucoma. In this regard, we explore cost-effective redfree fundus imaging for RNFLD detection to be practically useful for computer-assisted early glaucoma risk assessment. RNFLD appears as a wedge shaped arcuate structure radiating from the optic disc. The very low contrast between RNFLD and background makes its visual detection quite challenging even by medical experts. In our study, we formulate a deep convolutional neural network (CNN) based patch classification strategy for RNFLD boundary localization. A large number of RNFLD and background image patches train the deep CNN model, which extracts sufficient discriminative information from the patches and results in accurate RNFLD boundary pixel classification. The proposed approach is found to achieve enhanced RNFLD detection performance with sensitivity of 0.8205 and false positive per image of 0.2000 on a newly created early glaucomatic fundus image database. � 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).
Appears in Collections:Research Publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.