On Performance Analysis Of Diabetic Retinopathy Classification

This paper describes the Classification of bulk OCT retinal fundus images of normal and diabetic retinopathy using the Intensity histogram features, Gray Level Co-Occurrence Matrix (GLCM), and the Gray Level Run Length Matrix (GLRLM) feature extraction techniques. Three features—Intensity histogram...

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Bibliographic Details
Published in:Electronic letters on computer vision and image analysis 2024-01, Vol.22 (2), p.12-25
Main Authors: S, Sanjayprabu, R, Sathish Kumar, Jafari, Saeid, R, Karthikamani
Format: Article
Language:English
Subjects:
GLCM
GLRLM
GMM
IHF
OCT
Online Access:Get full text
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Summary:This paper describes the Classification of bulk OCT retinal fundus images of normal and diabetic retinopathy using the Intensity histogram features, Gray Level Co-Occurrence Matrix (GLCM), and the Gray Level Run Length Matrix (GLRLM) feature extraction techniques. Three features—Intensity histogram features, GLCM, and GLRLM were taken and, that features were compared fairly. A total of 301 bulk OCT retinal fundus color images were taken for two different varieties which are normal and diabetic retinopathy. For classification and feature extraction, a filtered image output based on a fourth-order PDE is used. Using OCT retinal fundus images, the most effective feature extraction method is identified.
ISSN:1577-5097
1577-5097
DOI:10.5565/rev/elcvia.1677