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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-9917826 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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