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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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
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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.
AbstractList	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. 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. 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. 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.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.
ArticleNumber	299
Author	Bhandary, Sulatha V. Raghavendra, U. Acharya, U. Rajendra Ciaccio, Edward J. Rao, Tejaswi N. Gudigar, Anjan
Author_xml	– sequence: 1 givenname: U. surname: Raghavendra fullname: Raghavendra, U. organization: Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education – sequence: 2 givenname: Anjan surname: Gudigar fullname: Gudigar, Anjan email: anjan.gudigar@manipal.edu organization: Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education – sequence: 3 givenname: Sulatha V. surname: Bhandary fullname: Bhandary, Sulatha V. organization: Department of Ophthalmology, Kasturba Medical College, Manipal Academy of Higher Education – sequence: 4 givenname: Tejaswi N. surname: Rao fullname: Rao, Tejaswi N. organization: Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education – sequence: 5 givenname: Edward J. surname: Ciaccio fullname: Ciaccio, Edward J. organization: Department of Medicine, Columbia University – sequence: 6 givenname: U. Rajendra surname: Acharya fullname: Acharya, U. Rajendra organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Department of Biomedical Engineering, School of Science and Technology, SUSS University, School of Medicine, Faculty of Health and Medical Sciences, Taylor’s University
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SubjectTerms	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
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Title	A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images
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