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Discriminative-Region Multi-Label Classification of Ultra-Widefield Fundus Images
Ultra-widefield fundus image (UFI) has become a crucial tool for ophthalmologists in diagnosing ocular diseases because of its ability to capture a wide field of the retina. Nevertheless, detecting and classifying multiple diseases within this imaging modality continues to pose a significant challen...
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/PMC10525847/ https://www.ncbi.nlm.nih.gov/pubmed/37760150 http://dx.doi.org/10.3390/bioengineering10091048 |
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author | Pham, Van-Nguyen Le, Duc-Tai Bum, Junghyun Kim, Seong Ho Song, Su Jeong Choo, Hyunseung |
author_facet | Pham, Van-Nguyen Le, Duc-Tai Bum, Junghyun Kim, Seong Ho Song, Su Jeong Choo, Hyunseung |
author_sort | Pham, Van-Nguyen |
collection | PubMed |
description | Ultra-widefield fundus image (UFI) has become a crucial tool for ophthalmologists in diagnosing ocular diseases because of its ability to capture a wide field of the retina. Nevertheless, detecting and classifying multiple diseases within this imaging modality continues to pose a significant challenge for ophthalmologists. An automated disease classification system for UFI can support ophthalmologists in making faster and more precise diagnoses. However, existing works for UFI classification often focus on a single disease or assume each image only contains one disease when tackling multi-disease issues. Furthermore, the distinctive characteristics of each disease are typically not utilized to improve the performance of the classification systems. To address these limitations, we propose a novel approach that leverages disease-specific regions of interest for the multi-label classification of UFI. Our method uses three regions, including the optic disc area, the macula area, and the entire UFI, which serve as the most informative regions for diagnosing one or multiple ocular diseases. Experimental results on a dataset comprising 5930 UFIs with six common ocular diseases showcase that our proposed approach attains exceptional performance, with the area under the receiver operating characteristic curve scores for each class spanning from 95.07% to 99.14%. These results not only surpass existing state-of-the-art methods but also exhibit significant enhancements, with improvements of up to 5.29%. These results demonstrate the potential of our method to provide ophthalmologists with valuable information for early and accurate diagnosis of ocular diseases, ultimately leading to improved patient outcomes. |
format | Online Article Text |
id | pubmed-10525847 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105258472023-09-28 Discriminative-Region Multi-Label Classification of Ultra-Widefield Fundus Images Pham, Van-Nguyen Le, Duc-Tai Bum, Junghyun Kim, Seong Ho Song, Su Jeong Choo, Hyunseung Bioengineering (Basel) Article Ultra-widefield fundus image (UFI) has become a crucial tool for ophthalmologists in diagnosing ocular diseases because of its ability to capture a wide field of the retina. Nevertheless, detecting and classifying multiple diseases within this imaging modality continues to pose a significant challenge for ophthalmologists. An automated disease classification system for UFI can support ophthalmologists in making faster and more precise diagnoses. However, existing works for UFI classification often focus on a single disease or assume each image only contains one disease when tackling multi-disease issues. Furthermore, the distinctive characteristics of each disease are typically not utilized to improve the performance of the classification systems. To address these limitations, we propose a novel approach that leverages disease-specific regions of interest for the multi-label classification of UFI. Our method uses three regions, including the optic disc area, the macula area, and the entire UFI, which serve as the most informative regions for diagnosing one or multiple ocular diseases. Experimental results on a dataset comprising 5930 UFIs with six common ocular diseases showcase that our proposed approach attains exceptional performance, with the area under the receiver operating characteristic curve scores for each class spanning from 95.07% to 99.14%. These results not only surpass existing state-of-the-art methods but also exhibit significant enhancements, with improvements of up to 5.29%. These results demonstrate the potential of our method to provide ophthalmologists with valuable information for early and accurate diagnosis of ocular diseases, ultimately leading to improved patient outcomes. MDPI 2023-09-06 /pmc/articles/PMC10525847/ /pubmed/37760150 http://dx.doi.org/10.3390/bioengineering10091048 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 Pham, Van-Nguyen Le, Duc-Tai Bum, Junghyun Kim, Seong Ho Song, Su Jeong Choo, Hyunseung Discriminative-Region Multi-Label Classification of Ultra-Widefield Fundus Images |
title | Discriminative-Region Multi-Label Classification of Ultra-Widefield Fundus Images |
title_full | Discriminative-Region Multi-Label Classification of Ultra-Widefield Fundus Images |
title_fullStr | Discriminative-Region Multi-Label Classification of Ultra-Widefield Fundus Images |
title_full_unstemmed | Discriminative-Region Multi-Label Classification of Ultra-Widefield Fundus Images |
title_short | Discriminative-Region Multi-Label Classification of Ultra-Widefield Fundus Images |
title_sort | discriminative-region multi-label classification of ultra-widefield fundus images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10525847/ https://www.ncbi.nlm.nih.gov/pubmed/37760150 http://dx.doi.org/10.3390/bioengineering10091048 |
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