A Novel Approach of Diabetic Retinopathy Early Detection Based on Multifractal Geometry Analysis for OCTA Macular Images Using Support Vector Machine

68Citations
Citations of this article
98Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Diabetic Retinopathy (DR) is a complication of diabetes that affects the eyes. It is caused by blood vessel damage of the light-sensitive tissue at the back of the retina. Neovascularization are emerged and the small blood vessels are blocked. The prevention or delaying vision loss can be obtained by DR early detection. The retinal microvascular network as a biological system has its own multifractal features as generalized dimensions, lacunarity and singularity spectrum. In this study, a novel approach for DR early detection based on the multifractal geometry has been proposed in some details. Analyzing the macular optical coherence tomography angiography (OCTA) images for diagnosing early non-proliferative diabetic retinopathy (NPDR). Using a supervised machine learning method as a Support Vector Machine (SVM) algorithm to automate the diagnosis process and improving the resultant accuracy. The classification technique had achieved 98.5 % accuracy. This approach also can classify easily other diabetic retinopathy stages or other retinal diseases, which affect the vessels or neovascularization distribution.

Cite

CITATION STYLE

APA

Abdelsalam, M. M., & Zahran, M. A. (2021). A Novel Approach of Diabetic Retinopathy Early Detection Based on Multifractal Geometry Analysis for OCTA Macular Images Using Support Vector Machine. IEEE Access, 9, 22844–22858. https://doi.org/10.1109/ACCESS.2021.3054743

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free