On Performance Analysis Of Diabetic Retinopathy Classification

• S, Sanjayprabu ^[1] ; R, Sathish Kumar ; Jafari, Saeid ; R, Karthikamani
  1. [1] Sri Ramakrishna Mission Vidyalaya College of Arts and Science
• Localización: ELCVIA. Electronic letters on computer vision and image analysis, ISSN-e 1577-5097, Vol. 22, Nº. 2, 2023 (Ejemplar dedicado a: Current Issue), págs. 12-25
• Idioma: inglés
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• Resumen

  • 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.