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Glaucoma Image Classification Using Entropy Feature and Maximum Likelihood Classifier

Journal of physics. Conference series, 2021-07, Vol.1964 (4), p.42075 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2021. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/1964/4/042075

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  • Title:
    Glaucoma Image Classification Using Entropy Feature and Maximum Likelihood Classifier
  • Author: Rebinth, Anisha ; Mohan Kumar, S ; Kumanan, T ; Varaprasad, G
  • Subjects: Classification ; Classifiers ; Entropy ; Entropy (Information theory) ; Entropy feature ; Feature extraction ; Glaucoma ; Image classification ; Maximum likelihood classifier ; Ranklet transform
  • Is Part Of: Journal of physics. Conference series, 2021-07, Vol.1964 (4), p.42075
  • Description: Abstract In general, the nerve that links the eye to the brain is affected because of high eye pressure. The most common kind of glaucoma sometimes has no other symptoms than a gradual loss of vision. In this study, the Glaucoma Image Classification (GIC) is made by using different entropy features and Maximum Likelihood Classifier (MLC). Initially, the input fundus images are decomposed by using rankles transform, then the entropy features like sample entropy, Shannon entropy and approximate entropy are used to extract features. Finally, MLC is applied for classification. The GIC scheme’s function produces the classification accuracy of 96 % by using Shannon entropy feature and MLC.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/1964/4/042075
  • Source: IOPscience (Open Access)
    Institute of Physics Open Access Journal Titles
    GFMER Free Medical Journals
    ProQuest Central
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