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Widen the Applicability of a Convolutional Neural-Network-Assisted Glaucoma Detection Algorithm of Limited Training Images across Different Datasets
Automated glaucoma detection using deep learning may increase the diagnostic rate of glaucoma to prevent blindness, but generalizable models are currently unavailable despite the use of huge training datasets. This study aims to evaluate the performance of a convolutional neural network (CNN) classi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9219722/ https://www.ncbi.nlm.nih.gov/pubmed/35740336 http://dx.doi.org/10.3390/biomedicines10061314 |
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author | Ko, Yu-Chieh Chen, Wei-Shiang Chen, Hung-Hsun Hsu, Tsui-Kang Chen, Ying-Chi Liu, Catherine Jui-Ling Lu, Henry Horng-Shing |
author_facet | Ko, Yu-Chieh Chen, Wei-Shiang Chen, Hung-Hsun Hsu, Tsui-Kang Chen, Ying-Chi Liu, Catherine Jui-Ling Lu, Henry Horng-Shing |
author_sort | Ko, Yu-Chieh |
collection | PubMed |
description | Automated glaucoma detection using deep learning may increase the diagnostic rate of glaucoma to prevent blindness, but generalizable models are currently unavailable despite the use of huge training datasets. This study aims to evaluate the performance of a convolutional neural network (CNN) classifier trained with a limited number of high-quality fundus images in detecting glaucoma and methods to improve its performance across different datasets. A CNN classifier was constructed using EfficientNet B3 and 944 images collected from one medical center (core model) and externally validated using three datasets. The performance of the core model was compared with (1) the integrated model constructed by using all training images from the four datasets and (2) the dataset-specific model built by fine-tuning the core model with training images from the external datasets. The diagnostic accuracy of the core model was 95.62% but dropped to ranges of 52.5–80.0% on the external datasets. Dataset-specific models exhibited superior diagnostic performance on the external datasets compared to other models, with a diagnostic accuracy of 87.50–92.5%. The findings suggest that dataset-specific tuning of the core CNN classifier effectively improves its applicability across different datasets when increasing training images fails to achieve generalization. |
format | Online Article Text |
id | pubmed-9219722 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92197222022-06-24 Widen the Applicability of a Convolutional Neural-Network-Assisted Glaucoma Detection Algorithm of Limited Training Images across Different Datasets Ko, Yu-Chieh Chen, Wei-Shiang Chen, Hung-Hsun Hsu, Tsui-Kang Chen, Ying-Chi Liu, Catherine Jui-Ling Lu, Henry Horng-Shing Biomedicines Article Automated glaucoma detection using deep learning may increase the diagnostic rate of glaucoma to prevent blindness, but generalizable models are currently unavailable despite the use of huge training datasets. This study aims to evaluate the performance of a convolutional neural network (CNN) classifier trained with a limited number of high-quality fundus images in detecting glaucoma and methods to improve its performance across different datasets. A CNN classifier was constructed using EfficientNet B3 and 944 images collected from one medical center (core model) and externally validated using three datasets. The performance of the core model was compared with (1) the integrated model constructed by using all training images from the four datasets and (2) the dataset-specific model built by fine-tuning the core model with training images from the external datasets. The diagnostic accuracy of the core model was 95.62% but dropped to ranges of 52.5–80.0% on the external datasets. Dataset-specific models exhibited superior diagnostic performance on the external datasets compared to other models, with a diagnostic accuracy of 87.50–92.5%. The findings suggest that dataset-specific tuning of the core CNN classifier effectively improves its applicability across different datasets when increasing training images fails to achieve generalization. MDPI 2022-06-03 /pmc/articles/PMC9219722/ /pubmed/35740336 http://dx.doi.org/10.3390/biomedicines10061314 Text en © 2022 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 Ko, Yu-Chieh Chen, Wei-Shiang Chen, Hung-Hsun Hsu, Tsui-Kang Chen, Ying-Chi Liu, Catherine Jui-Ling Lu, Henry Horng-Shing Widen the Applicability of a Convolutional Neural-Network-Assisted Glaucoma Detection Algorithm of Limited Training Images across Different Datasets |
title | Widen the Applicability of a Convolutional Neural-Network-Assisted Glaucoma Detection Algorithm of Limited Training Images across Different Datasets |
title_full | Widen the Applicability of a Convolutional Neural-Network-Assisted Glaucoma Detection Algorithm of Limited Training Images across Different Datasets |
title_fullStr | Widen the Applicability of a Convolutional Neural-Network-Assisted Glaucoma Detection Algorithm of Limited Training Images across Different Datasets |
title_full_unstemmed | Widen the Applicability of a Convolutional Neural-Network-Assisted Glaucoma Detection Algorithm of Limited Training Images across Different Datasets |
title_short | Widen the Applicability of a Convolutional Neural-Network-Assisted Glaucoma Detection Algorithm of Limited Training Images across Different Datasets |
title_sort | widen the applicability of a convolutional neural-network-assisted glaucoma detection algorithm of limited training images across different datasets |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9219722/ https://www.ncbi.nlm.nih.gov/pubmed/35740336 http://dx.doi.org/10.3390/biomedicines10061314 |
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