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The Classification of Common Macular Diseases Using Deep Learning on Optical Coherence Tomography Images with and without Prior Automated Segmentation

We compared the performance of deep learning (DL) in the classification of optical coherence tomography (OCT) images of macular diseases between automated classification alone and in combination with automated segmentation. OCT images were collected from patients with neovascular age-related macular...

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Autores principales: Kaothanthong, Natsuda, Limwattanayingyong, Jirawut, Silpa-archa, Sukhum, Tadarati, Mongkol, Amphornphruet, Atchara, Singhanetr, Panisa, Lalitwongsa, Pawas, Chantangphol, Pantid, Amornpetchsathaporn, Anyarak, Chainakul, Methaphon, Ruamviboonsuk, Paisan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858554/
https://www.ncbi.nlm.nih.gov/pubmed/36672999
http://dx.doi.org/10.3390/diagnostics13020189
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author Kaothanthong, Natsuda
Limwattanayingyong, Jirawut
Silpa-archa, Sukhum
Tadarati, Mongkol
Amphornphruet, Atchara
Singhanetr, Panisa
Lalitwongsa, Pawas
Chantangphol, Pantid
Amornpetchsathaporn, Anyarak
Chainakul, Methaphon
Ruamviboonsuk, Paisan
author_facet Kaothanthong, Natsuda
Limwattanayingyong, Jirawut
Silpa-archa, Sukhum
Tadarati, Mongkol
Amphornphruet, Atchara
Singhanetr, Panisa
Lalitwongsa, Pawas
Chantangphol, Pantid
Amornpetchsathaporn, Anyarak
Chainakul, Methaphon
Ruamviboonsuk, Paisan
author_sort Kaothanthong, Natsuda
collection PubMed
description We compared the performance of deep learning (DL) in the classification of optical coherence tomography (OCT) images of macular diseases between automated classification alone and in combination with automated segmentation. OCT images were collected from patients with neovascular age-related macular degeneration, polypoidal choroidal vasculopathy, diabetic macular edema, retinal vein occlusion, cystoid macular edema in Irvine-Gass syndrome, and other macular diseases, along with the normal fellow eyes. A total of 14,327 OCT images were used to train DL models. Three experiments were conducted: classification alone (CA), use of automated segmentation of the OCT images by RelayNet, and the graph-cut technique before the classification (combination method 1 (CM1) and 2 (CM2), respectively). For validation of classification of the macular diseases, the sensitivity, specificity, and accuracy of CA were found at 62.55%, 95.16%, and 93.14%, respectively, whereas the sensitivity, specificity, and accuracy of CM1 were found at 72.90%, 96.20%, and 93.92%, respectively, and of CM2 at 71.36%, 96.42%, and 94.80%, respectively. The accuracy of CM2 was statistically higher than that of CA (p = 0.05878). All three methods achieved AUC at 97%. Applying DL for segmentation of OCT images prior to classification of the images by another DL model may improve the performance of the classification.
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spelling pubmed-98585542023-01-21 The Classification of Common Macular Diseases Using Deep Learning on Optical Coherence Tomography Images with and without Prior Automated Segmentation Kaothanthong, Natsuda Limwattanayingyong, Jirawut Silpa-archa, Sukhum Tadarati, Mongkol Amphornphruet, Atchara Singhanetr, Panisa Lalitwongsa, Pawas Chantangphol, Pantid Amornpetchsathaporn, Anyarak Chainakul, Methaphon Ruamviboonsuk, Paisan Diagnostics (Basel) Article We compared the performance of deep learning (DL) in the classification of optical coherence tomography (OCT) images of macular diseases between automated classification alone and in combination with automated segmentation. OCT images were collected from patients with neovascular age-related macular degeneration, polypoidal choroidal vasculopathy, diabetic macular edema, retinal vein occlusion, cystoid macular edema in Irvine-Gass syndrome, and other macular diseases, along with the normal fellow eyes. A total of 14,327 OCT images were used to train DL models. Three experiments were conducted: classification alone (CA), use of automated segmentation of the OCT images by RelayNet, and the graph-cut technique before the classification (combination method 1 (CM1) and 2 (CM2), respectively). For validation of classification of the macular diseases, the sensitivity, specificity, and accuracy of CA were found at 62.55%, 95.16%, and 93.14%, respectively, whereas the sensitivity, specificity, and accuracy of CM1 were found at 72.90%, 96.20%, and 93.92%, respectively, and of CM2 at 71.36%, 96.42%, and 94.80%, respectively. The accuracy of CM2 was statistically higher than that of CA (p = 0.05878). All three methods achieved AUC at 97%. Applying DL for segmentation of OCT images prior to classification of the images by another DL model may improve the performance of the classification. MDPI 2023-01-04 /pmc/articles/PMC9858554/ /pubmed/36672999 http://dx.doi.org/10.3390/diagnostics13020189 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
Kaothanthong, Natsuda
Limwattanayingyong, Jirawut
Silpa-archa, Sukhum
Tadarati, Mongkol
Amphornphruet, Atchara
Singhanetr, Panisa
Lalitwongsa, Pawas
Chantangphol, Pantid
Amornpetchsathaporn, Anyarak
Chainakul, Methaphon
Ruamviboonsuk, Paisan
The Classification of Common Macular Diseases Using Deep Learning on Optical Coherence Tomography Images with and without Prior Automated Segmentation
title The Classification of Common Macular Diseases Using Deep Learning on Optical Coherence Tomography Images with and without Prior Automated Segmentation
title_full The Classification of Common Macular Diseases Using Deep Learning on Optical Coherence Tomography Images with and without Prior Automated Segmentation
title_fullStr The Classification of Common Macular Diseases Using Deep Learning on Optical Coherence Tomography Images with and without Prior Automated Segmentation
title_full_unstemmed The Classification of Common Macular Diseases Using Deep Learning on Optical Coherence Tomography Images with and without Prior Automated Segmentation
title_short The Classification of Common Macular Diseases Using Deep Learning on Optical Coherence Tomography Images with and without Prior Automated Segmentation
title_sort classification of common macular diseases using deep learning on optical coherence tomography images with and without prior automated segmentation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858554/
https://www.ncbi.nlm.nih.gov/pubmed/36672999
http://dx.doi.org/10.3390/diagnostics13020189
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