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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...
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creator | Sharafeldeen, Ahmed Elgafi, Mahmoud Elnakib, Ahmed Mahmoud, Ali Elgarayhi, Ahmed Alghamdi, Norah S. Sallah, Mohammed El-Baz, Ayman |
description | 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. |
doi_str_mv | 10.1109/ISBI53787.2023.10230785 |
format | conference_proceeding |
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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%. 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These results demonstrate the method's potential for DR detection utilizing OCT images.</description><subject>CAD System</subject><subject>Diabetic retinopathy</subject><subject>Neural Network</subject><subject>Neural networks</subject><subject>Optical Coherence Tomograph</subject><subject>Optical coherence tomography</subject><subject>Optical losses</subject><subject>Reflectivity</subject><subject>Thickness</subject><subject>Three-dimensional displays</subject><subject>Visualization</subject><issn>1945-8452</issn><isbn>9781665473583</isbn><isbn>1665473584</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2023</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1j8FKxDAURaMgOIz9A8H8QOtLXtKkS20dLQwMaF0PafqiEe0MbVzM31tQ7-LcxYELl7EbAYUQUN22L_etRmNNIUFiIRaAsfqMZZWxoiy1MqgtnrOVqJTOrdLykmXz_AFLjFIIasVUE11PKXr-vHA8HF16P_GGEvkUDyN_neP4xrHhu7rjG3Lpe6L5il0E9zlT9tdr1m0euvop3-4e2_pum0cJKuVDCBUoKP0gLHlwgFj2WPreS43o1VD5oCU5IAXklQ8hoF2U6VEIA7hm17-zkYj2xyl-uem0__-JP2EERik</recordid><startdate>20230418</startdate><enddate>20230418</enddate><creator>Sharafeldeen, Ahmed</creator><creator>Elgafi, Mahmoud</creator><creator>Elnakib, Ahmed</creator><creator>Mahmoud, Ali</creator><creator>Elgarayhi, Ahmed</creator><creator>Alghamdi, Norah S.</creator><creator>Sallah, Mohammed</creator><creator>El-Baz, Ayman</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20230418</creationdate><title>Diabetic Retinopathy Detection Using 3D OCT Features</title><author>Sharafeldeen, Ahmed ; Elgafi, Mahmoud ; Elnakib, Ahmed ; Mahmoud, Ali ; Elgarayhi, Ahmed ; Alghamdi, Norah S. ; Sallah, Mohammed ; El-Baz, Ayman</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i204t-dff90406cd18ec0a0336b36cbc2533c4d9cf52ea0e40ec4cfff382537b311703</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2023</creationdate><topic>CAD System</topic><topic>Diabetic retinopathy</topic><topic>Neural Network</topic><topic>Neural networks</topic><topic>Optical Coherence Tomograph</topic><topic>Optical coherence tomography</topic><topic>Optical losses</topic><topic>Reflectivity</topic><topic>Thickness</topic><topic>Three-dimensional displays</topic><topic>Visualization</topic><toplevel>online_resources</toplevel><creatorcontrib>Sharafeldeen, Ahmed</creatorcontrib><creatorcontrib>Elgafi, Mahmoud</creatorcontrib><creatorcontrib>Elnakib, Ahmed</creatorcontrib><creatorcontrib>Mahmoud, Ali</creatorcontrib><creatorcontrib>Elgarayhi, Ahmed</creatorcontrib><creatorcontrib>Alghamdi, Norah S.</creatorcontrib><creatorcontrib>Sallah, Mohammed</creatorcontrib><creatorcontrib>El-Baz, Ayman</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library Online</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Sharafeldeen, Ahmed</au><au>Elgafi, Mahmoud</au><au>Elnakib, Ahmed</au><au>Mahmoud, Ali</au><au>Elgarayhi, Ahmed</au><au>Alghamdi, Norah S.</au><au>Sallah, Mohammed</au><au>El-Baz, Ayman</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Diabetic Retinopathy Detection Using 3D OCT Features</atitle><btitle>2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)</btitle><stitle>ISBI</stitle><date>2023-04-18</date><risdate>2023</risdate><spage>1</spage><epage>4</epage><pages>1-4</pages><eissn>1945-8452</eissn><eisbn>9781665473583</eisbn><eisbn>1665473584</eisbn><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.</abstract><pub>IEEE</pub><doi>10.1109/ISBI53787.2023.10230785</doi><tpages>4</tpages></addata></record> |
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subjects | CAD System Diabetic retinopathy Neural Network Neural networks Optical Coherence Tomograph Optical coherence tomography Optical losses Reflectivity Thickness Three-dimensional displays Visualization |
title | Diabetic Retinopathy Detection Using 3D OCT Features |
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