Retinal Disease Diagnosis Using Deep Learning on Ultra-Wide-Field Fundus Images

Ultra-wide-field fundus imaging (UFI) provides comprehensive visualization of crucial eye components, including the optic disk, fovea, and macula. This in-depth view facilitates doctors in accurately diagnosing diseases and recommending suitable treatments. This study investigated the application of...
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Autor*in:	Toan Duc Nguyen [verfasserIn] Duc-Tai Le [verfasserIn] Junghyun Bum [verfasserIn] Seongho Kim [verfasserIn] Su Jeong Song [verfasserIn] Hyunseung Choo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	medical image processing deep learning fundus image convolutional neural network vision transformer

Übergeordnetes Werk:	In: Diagnostics - MDPI AG, 2012, 14(2024), 1, p 105
Übergeordnetes Werk:	volume:14 ; year:2024 ; number:1, p 105

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/diagnostics14010105

Katalog-ID:	DOAJ097805904

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authorswithroles_txt_mv	Toan Duc Nguyen @@aut@@ Duc-Tai Le @@aut@@ Junghyun Bum @@aut@@ Seongho Kim @@aut@@ Su Jeong Song @@aut@@ Hyunseung Choo @@aut@@
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callnumber-first	R - Medicine
author	Toan Duc Nguyen
spellingShingle	Toan Duc Nguyen misc R5-920 misc medical image processing misc deep learning misc fundus image misc convolutional neural network misc vision transformer misc Medicine (General) Retinal Disease Diagnosis Using Deep Learning on Ultra-Wide-Field Fundus Images
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topic_title	R5-920 Retinal Disease Diagnosis Using Deep Learning on Ultra-Wide-Field Fundus Images medical image processing deep learning fundus image convolutional neural network vision transformer
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title	Retinal Disease Diagnosis Using Deep Learning on Ultra-Wide-Field Fundus Images
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title_sort	retinal disease diagnosis using deep learning on ultra-wide-field fundus images
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title_auth	Retinal Disease Diagnosis Using Deep Learning on Ultra-Wide-Field Fundus Images
abstract	Ultra-wide-field fundus imaging (UFI) provides comprehensive visualization of crucial eye components, including the optic disk, fovea, and macula. This in-depth view facilitates doctors in accurately diagnosing diseases and recommending suitable treatments. This study investigated the application of various deep learning models for detecting eye diseases using UFI. We developed an automated system that processes and enhances a dataset of 4697 images. Our approach involves brightness and contrast enhancement, followed by applying feature extraction, data augmentation and image classification, integrated with convolutional neural networks. These networks utilize layer-wise feature extraction and transfer learning from pre-trained models to accurately represent and analyze medical images. Among the five evaluated models, including ResNet152, Vision Transformer, InceptionResNetV2, RegNet and ConVNext, ResNet152 is the most effective, achieving a testing area under the curve (AUC) score of 96.47% (with a 95% confidence interval (CI) of 0.931–0.974). Additionally, the paper presents visualizations of the model’s predictions, including confidence scores and heatmaps that highlight the model’s focal points—particularly where lesions due to damage are evident. By streamlining the diagnosis process and providing intricate prediction details without human intervention, our system serves as a pivotal tool for ophthalmologists. This research underscores the compatibility and potential of utilizing ultra-wide-field images in conjunction with deep learning.
abstractGer	Ultra-wide-field fundus imaging (UFI) provides comprehensive visualization of crucial eye components, including the optic disk, fovea, and macula. This in-depth view facilitates doctors in accurately diagnosing diseases and recommending suitable treatments. This study investigated the application of various deep learning models for detecting eye diseases using UFI. We developed an automated system that processes and enhances a dataset of 4697 images. Our approach involves brightness and contrast enhancement, followed by applying feature extraction, data augmentation and image classification, integrated with convolutional neural networks. These networks utilize layer-wise feature extraction and transfer learning from pre-trained models to accurately represent and analyze medical images. Among the five evaluated models, including ResNet152, Vision Transformer, InceptionResNetV2, RegNet and ConVNext, ResNet152 is the most effective, achieving a testing area under the curve (AUC) score of 96.47% (with a 95% confidence interval (CI) of 0.931–0.974). Additionally, the paper presents visualizations of the model’s predictions, including confidence scores and heatmaps that highlight the model’s focal points—particularly where lesions due to damage are evident. By streamlining the diagnosis process and providing intricate prediction details without human intervention, our system serves as a pivotal tool for ophthalmologists. This research underscores the compatibility and potential of utilizing ultra-wide-field images in conjunction with deep learning.
abstract_unstemmed	Ultra-wide-field fundus imaging (UFI) provides comprehensive visualization of crucial eye components, including the optic disk, fovea, and macula. This in-depth view facilitates doctors in accurately diagnosing diseases and recommending suitable treatments. This study investigated the application of various deep learning models for detecting eye diseases using UFI. We developed an automated system that processes and enhances a dataset of 4697 images. Our approach involves brightness and contrast enhancement, followed by applying feature extraction, data augmentation and image classification, integrated with convolutional neural networks. These networks utilize layer-wise feature extraction and transfer learning from pre-trained models to accurately represent and analyze medical images. Among the five evaluated models, including ResNet152, Vision Transformer, InceptionResNetV2, RegNet and ConVNext, ResNet152 is the most effective, achieving a testing area under the curve (AUC) score of 96.47% (with a 95% confidence interval (CI) of 0.931–0.974). Additionally, the paper presents visualizations of the model’s predictions, including confidence scores and heatmaps that highlight the model’s focal points—particularly where lesions due to damage are evident. By streamlining the diagnosis process and providing intricate prediction details without human intervention, our system serves as a pivotal tool for ophthalmologists. This research underscores the compatibility and potential of utilizing ultra-wide-field images in conjunction with deep learning.
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title_short	Retinal Disease Diagnosis Using Deep Learning on Ultra-Wide-Field Fundus Images
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Retinal Disease Diagnosis Using Deep Learning on Ultra-Wide-Field Fundus Images

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