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...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 9 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=138200113&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138200113 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Sep2019 vid: 43 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138200113 138200113 138200113 10.1007/s10916-019-1427-x 138200113 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images. aug: au: Raghavendra, U. Gudigar, Anjan Bhandary, Sulatha V. Rao, Tejaswi N. Ciaccio, Edward J. Acharya, U. Rajendra affil: Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, 576104, Manipal, India sug: subj: Glaucoma Diagnosis Retina Radiography Neural Networks (Computer) Diagnosis, Computer Assisted Methods Machine Learning Autoencoder Human Models, Biological Early Diagnosis Decision Making, Clinical Minimum Data Set Algorithms Health Screening Vision Disorders Prevention and Control ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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