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Detecting Macular Disease Based on Optical Coherence Tomography Using a Deep Convolutional Network

Neovascular age-related macular degeneration (nAMD) and central serous chorioretinopathy (CSC) are two of the most common macular diseases. This study proposes a convolutional neural network (CNN)-based deep learning model for classifying the subtypes of nAMD (polypoidal choroidal vasculopathy, reti...

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Autores principales: Han, Jinyoung, Choi, Seong, Park, Ji In, Hwang, Joon Seo, Han, Jeong Mo, Ko, Junseo, Yoon, Jeewoo, Hwang, Daniel Duck-Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9917826/
https://www.ncbi.nlm.nih.gov/pubmed/36769653
http://dx.doi.org/10.3390/jcm12031005
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author Han, Jinyoung
Choi, Seong
Park, Ji In
Hwang, Joon Seo
Han, Jeong Mo
Ko, Junseo
Yoon, Jeewoo
Hwang, Daniel Duck-Jin
author_facet Han, Jinyoung
Choi, Seong
Park, Ji In
Hwang, Joon Seo
Han, Jeong Mo
Ko, Junseo
Yoon, Jeewoo
Hwang, Daniel Duck-Jin
author_sort Han, Jinyoung
collection PubMed
description Neovascular age-related macular degeneration (nAMD) and central serous chorioretinopathy (CSC) are two of the most common macular diseases. This study proposes a convolutional neural network (CNN)-based deep learning model for classifying the subtypes of nAMD (polypoidal choroidal vasculopathy, retinal angiomatous proliferation, and typical nAMD) and CSC (chronic CSC and acute CSC) and healthy individuals using single spectral–domain optical coherence tomography (SD–OCT) images. The proposed model was trained and tested using 6063 SD–OCT images from 521 patients and 47 healthy participants. We used three well-known CNN architectures (VGG–16, VGG–19, and ResNet) and two customized classification layers. Additionally, transfer learning and mix–up-based data augmentation were applied to improve robustness and accuracy. Our model demonstrated high accuracies of 99.7% and 91.1% in the nAMD and CSC classification and retinopathy (nAMD and CSC) subtype classification, including normal participants, respectively. Furthermore, we performed an external test to compare the classification accuracy with that of eight ophthalmologists, and our model showed the highest accuracy. The region determined to be important for classification by the model was confirmed using gradient-weighted class activation mapping. The model’s clinical criteria were similar to that of the ophthalmologists.
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spelling pubmed-99178262023-02-11 Detecting Macular Disease Based on Optical Coherence Tomography Using a Deep Convolutional Network Han, Jinyoung Choi, Seong Park, Ji In Hwang, Joon Seo Han, Jeong Mo Ko, Junseo Yoon, Jeewoo Hwang, Daniel Duck-Jin J Clin Med Article Neovascular age-related macular degeneration (nAMD) and central serous chorioretinopathy (CSC) are two of the most common macular diseases. This study proposes a convolutional neural network (CNN)-based deep learning model for classifying the subtypes of nAMD (polypoidal choroidal vasculopathy, retinal angiomatous proliferation, and typical nAMD) and CSC (chronic CSC and acute CSC) and healthy individuals using single spectral–domain optical coherence tomography (SD–OCT) images. The proposed model was trained and tested using 6063 SD–OCT images from 521 patients and 47 healthy participants. We used three well-known CNN architectures (VGG–16, VGG–19, and ResNet) and two customized classification layers. Additionally, transfer learning and mix–up-based data augmentation were applied to improve robustness and accuracy. Our model demonstrated high accuracies of 99.7% and 91.1% in the nAMD and CSC classification and retinopathy (nAMD and CSC) subtype classification, including normal participants, respectively. Furthermore, we performed an external test to compare the classification accuracy with that of eight ophthalmologists, and our model showed the highest accuracy. The region determined to be important for classification by the model was confirmed using gradient-weighted class activation mapping. The model’s clinical criteria were similar to that of the ophthalmologists. MDPI 2023-01-28 /pmc/articles/PMC9917826/ /pubmed/36769653 http://dx.doi.org/10.3390/jcm12031005 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Han, Jinyoung
Choi, Seong
Park, Ji In
Hwang, Joon Seo
Han, Jeong Mo
Ko, Junseo
Yoon, Jeewoo
Hwang, Daniel Duck-Jin
Detecting Macular Disease Based on Optical Coherence Tomography Using a Deep Convolutional Network
title Detecting Macular Disease Based on Optical Coherence Tomography Using a Deep Convolutional Network
title_full Detecting Macular Disease Based on Optical Coherence Tomography Using a Deep Convolutional Network
title_fullStr Detecting Macular Disease Based on Optical Coherence Tomography Using a Deep Convolutional Network
title_full_unstemmed Detecting Macular Disease Based on Optical Coherence Tomography Using a Deep Convolutional Network
title_short Detecting Macular Disease Based on Optical Coherence Tomography Using a Deep Convolutional Network
title_sort detecting macular disease based on optical coherence tomography using a deep convolutional network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9917826/
https://www.ncbi.nlm.nih.gov/pubmed/36769653
http://dx.doi.org/10.3390/jcm12031005
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