Diabetic Retinopathy Detection Using 3D OCT Features

Ahmed Sharafeldeen, Mahmoud Elgafi, Ahmed Elnakib, Ali Mahmoud, Ahmed Elgarayhi, Norah S. Alghamdi, Mohammed Sallah, Ayman El-Baz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


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.

Original languageEnglish (US)
Title of host publication2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
PublisherIEEE Computer Society
ISBN (Electronic)9781665473583
StatePublished - 2023
Event20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, Colombia
Duration: Apr 18 2023Apr 21 2023

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference20th IEEE International Symposium on Biomedical Imaging, ISBI 2023

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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