A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images

Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential...

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Bibliographic Details
Published in:Journal of medical systems Vol. 43; no. 9; pp. 299 - 9
Main Authors: Raghavendra, U., Gudigar, Anjan, Bhandary, Sulatha V., Rao, Tejaswi N., Ciaccio, Edward J., Acharya, U. Rajendra
Format: Journal Article
Language:English
Published: New York Springer US 01.09.2019
Springer Nature B.V
Subjects:
Adult
Algorithms
Case-Control Studies
Diagnostic Techniques, Ophthalmological
Digital imaging
Female
Fundus Oculi
Glaucoma
Health Informatics
Health Informatics and Computer Vision
Health Sciences
Humans
Image & Signal Processing
Image Interpretation, Computer-Assisted - methods
Intraocular pressure
Learning algorithms
Machine learning
Male
Medicine
Medicine & Public Health
Middle Aged
Pattern Recognition, Automated - methods
Quantitative analysis
Recent Advances in Deep Learning for Biomedical Signal Processing
Statistics for Life Sciences
Test procedures
Tomography, Optical Coherence - methods
Vision
Glaucoma
Sparse autoencoder
Cascade
CAD
ISSN:0148-5598, 1573-689X, 1573-689X
Online Access:Get full text
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Summary:Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential to identify glaucoma early through quantitative analysis of digital fundus images. Preparing an effective model for CAD requires a large database. This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach. An autoencoder is trained to determine effective and important features from fundus images. These features are used to develop classes of glaucoma for testing. The method achieved an F  −  measure value of 0.95 utilizing 1426 digital fundus images (589 control and 837 glaucoma). The efficacy of the system is evident, and is suggestive of its possible utility as an additional tool for verification of clinical decisions.
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ISSN:0148-5598
1573-689X
1573-689X
DOI:10.1007/s10916-019-1427-x