A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images.

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ID: 29304
2019
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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.
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raghavendra2019ajournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Raghavendra, U;Gudigar, Anjan;Bhandary, Sulatha V;Rao, Tejaswi N;Ciaccio, Edward J;Acharya, U Rajendra;
Journal Journal of medical systems
Year 2019
DOI
10.1007/s10916-019-1427-x
URL
Keywords Keywords not found

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