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- Title
A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images.
- Authors
Raghavendra, U.; Gudigar, Anjan; Bhandary, Sulatha V.; Rao, Tejaswi N.; Ciaccio, Edward J.; Acharya, U. Rajendra
- Abstract
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.
- Subjects
GLAUCOMA diagnosis; ALGORITHMS; AUTOMATION; BIOLOGICAL models; DATABASE management; MACHINE learning; MEDICAL screening; ARTIFICIAL neural networks; RETINA; VISION disorders; DECISION making in clinical medicine; EARLY diagnosis; COMPUTER-aided diagnosis
- Publication
Journal of Medical Systems, 2019, Vol 43, Issue 9, pN.PAG
- ISSN
0148-5598
- Publication type
Article
- DOI
10.1007/s10916-019-1427-x