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

doi:10.1109/ISBI53787.2023.10230785

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

School of Engineering (Behrend)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Scopus citations

Abstract

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 language	English (US)
Title of host publication	2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
Publisher	IEEE Computer Society
ISBN (Electronic)	9781665473583
DOIs	https://doi.org/10.1109/ISBI53787.2023.10230785
State	Published - 2023
Event	20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, Colombia Duration: Apr 18 2023 → Apr 21 2023

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2023-April
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
Country/Territory	Colombia
City	Cartagena
Period	4/18/23 → 4/21/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/ISBI53787.2023.10230785

Cite this

Sharafeldeen, A., Elgafi, M., Elnakib, A., Mahmoud, A., Elgarayhi, A., Alghamdi, N. S., Sallah, M., & El-Baz, A. (2023). Diabetic Retinopathy Detection Using 3D OCT Features. In 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2023-April). IEEE Computer Society. https://doi.org/10.1109/ISBI53787.2023.10230785

@inproceedings{1e75c08cc10f44cfa82fd2319d175445,

title = "Diabetic Retinopathy Detection Using 3D OCT Features",

abstract = "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.",

author = "Ahmed Sharafeldeen and Mahmoud Elgafi and Ahmed Elnakib and Ali Mahmoud and Ahmed Elgarayhi and Alghamdi, \{Norah S.\} and Mohammed Sallah and Ayman El-Baz",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 ; Conference date: 18-04-2023 Through 21-04-2023",

year = "2023",

doi = "10.1109/ISBI53787.2023.10230785",

language = "English (US)",

series = "Proceedings - International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

booktitle = "2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023",

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}

Sharafeldeen, A, Elgafi, M, Elnakib, A, Mahmoud, A, Elgarayhi, A, Alghamdi, NS, Sallah, M & El-Baz, A 2023, Diabetic Retinopathy Detection Using 3D OCT Features. in 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023. Proceedings - International Symposium on Biomedical Imaging, vol. 2023-April, IEEE Computer Society, 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023, Cartagena, Colombia, 4/18/23. https://doi.org/10.1109/ISBI53787.2023.10230785

Diabetic Retinopathy Detection Using 3D OCT Features. / Sharafeldeen, Ahmed; Elgafi, Mahmoud; Elnakib, Ahmed et al.
2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023. IEEE Computer Society, 2023. (Proceedings - International Symposium on Biomedical Imaging; Vol. 2023-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Diabetic Retinopathy Detection Using 3D OCT Features

AU - Sharafeldeen, Ahmed

AU - Elgafi, Mahmoud

AU - Elnakib, Ahmed

AU - Mahmoud, Ali

AU - Elgarayhi, Ahmed

AU - Alghamdi, Norah S.

AU - Sallah, Mohammed

AU - El-Baz, Ayman

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:85172113488

T3 - Proceedings - International Symposium on Biomedical Imaging

BT - 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023

PB - IEEE Computer Society

T2 - 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023

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ER -

Diabetic Retinopathy Detection Using 3D OCT Features

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

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