Diabetic Retinopathy Detection Using 3D OCT Features

If untreated, diabetic retinopathy (DR) can result in a severe health complication, leading to visual loss. This study focuses on developing a computer-assisted diagnostic (CAD) system that utilizes 3D optical coherence tomography (OCT) images for detecting DR. To begin with, the 3D OCT images are s...

Full description

Saved in:
Bibliographic Details
Main Authors: Sharafeldeen, Ahmed, Elgafi, Mahmoud, Elnakib, Ahmed, Mahmoud, Ali, Elgarayhi, Ahmed, Alghamdi, Norah S., Sallah, Mohammed, El-Baz, Ayman
Format: Conference Proceeding
Language:English
Subjects:
CAD System
Diabetic retinopathy
Neural Network
Neural networks
Optical Coherence Tomograph
Optical coherence tomography
Optical losses
Reflectivity
Thickness
Three-dimensional displays
Visualization
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:If untreated, diabetic retinopathy (DR) can result in a severe health complication, leading to visual loss. This study focuses on developing a computer-assisted diagnostic (CAD) system that utilizes 3D optical coherence tomography (OCT) images for detecting DR. To begin with, the 3D OCT images are subjected to a process where the retinal layers are isolated from the input. Following this, from each individual retinal layer, two key 3D characteristics, namely thickness and first-order reflectivity, are computed. Eventually, classification is carried out using backpropagation neural networks. Utilizing 10-folds cross-validation on 188 cases, experiments validate the benefits of the developed system over competing approaches, with an accuracy of 94.74% ± 5.55%. These results demonstrate the method's potential for DR detection utilizing OCT images.
ISSN:1945-8452
DOI:10.1109/ISBI53787.2023.10230785