Classification of Retina Diseases from OCT using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8129
- @Article{Abdulrahman:2020:IJCA,
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author = "Hadeel Abdulrahman and Mohamed Khatib",
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title = "Classification of Retina Diseases from {OCT} using
Genetic Programming",
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journal = "International Journal of Computer Applications",
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year = "2020",
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volume = "177",
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number = "45",
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pages = "41--46",
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month = mar,
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keywords = "genetic algorithms, genetic programming, feature
extraction, Optical Coherence Tomography, OCT image
classification, OCT feature extraction",
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publisher = "Foundation of Computer Science (FCS), NY, USA",
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address = "New York, USA",
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ISSN = "0975-8887",
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URL = "https://www.ijcaonline.org/archives/volume177/number45/abdulrahman-2020-ijca-919973.pdf",
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URL = "http://www.ijcaonline.org/archives/volume177/number45/31212-2020919973",
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DOI = "doi:10.5120/ijca2020919973",
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size = "6 pages",
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abstract = "a fully automated method for feature extraction and
classification of retina diseases is implemented. The
main idea is to find a method that can extract the
important features from the Optical Coherence
Tomography (OCT) image, and acquire a higher
classification accuracy. The using of genetic
programming (GP) can achieve that aim. Genetic
programming is a good way to choose the best
combination of feature extraction methods from a set of
feature extraction methods and determine the proper
parameters for each one of the selected extraction
methods. 800 OCT images are used in the proposed
method, of the most three popular retinal diseases:
Choroidal neovascularization (CNV), Diabetic Macular
Edema (DME) and Drusen, beside the normal OCT images.
While the set of the feature extraction methods that is
used in this paper contains: Gabor filter, Local Binary
Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM),
histogram of the image, and Speed Up Robust Filter
(SURF). These methods are used for the both of global
and local feature extraction. After that the
classification process is achieved by the Support
Vector Machine (SVM). The proposed method performed
high accuracy as compared with the traditional
methods.",
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notes = "Also known as
\cite{10.5120/ijca2020919973,}
www.ijcaonline.org
Department of Artificial Intelligence, Faculty of
Informatics Engineering, Aleppo University, Syria",
- }
Genetic Programming entries for
Hadeel Abdulrahman
Mohamed M Khatib
Citations