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Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture

Accurate noninvasive diagnosis of retinal disorders is required for appropriate treatment or precision medicine. This work proposes a multi-stage classification network built on a multi-scale (pyramidal) feature ensemble architecture for retinal image classification using optical coherence tomograph...

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Autores principales: Akinniyi, Oluwatunmise, Rahman, Md Mahmudur, Sandhu, Harpal Singh, El-Baz, Ayman, Khalifa, Fahmi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10376573/
https://www.ncbi.nlm.nih.gov/pubmed/37508850
http://dx.doi.org/10.3390/bioengineering10070823
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author Akinniyi, Oluwatunmise
Rahman, Md Mahmudur
Sandhu, Harpal Singh
El-Baz, Ayman
Khalifa, Fahmi
author_facet Akinniyi, Oluwatunmise
Rahman, Md Mahmudur
Sandhu, Harpal Singh
El-Baz, Ayman
Khalifa, Fahmi
author_sort Akinniyi, Oluwatunmise
collection PubMed
description Accurate noninvasive diagnosis of retinal disorders is required for appropriate treatment or precision medicine. This work proposes a multi-stage classification network built on a multi-scale (pyramidal) feature ensemble architecture for retinal image classification using optical coherence tomography (OCT) images. First, a scale-adaptive neural network is developed to produce multi-scale inputs for feature extraction and ensemble learning. The larger input sizes yield more global information, while the smaller input sizes focus on local details. Then, a feature-rich pyramidal architecture is designed to extract multi-scale features as inputs using DenseNet as the backbone. The advantage of the hierarchical structure is that it allows the system to extract multi-scale, information-rich features for the accurate classification of retinal disorders. Evaluation on two public OCT datasets containing normal and abnormal retinas (e.g., diabetic macular edema (DME), choroidal neovascularization (CNV), age-related macular degeneration (AMD), and Drusen) and comparison against recent networks demonstrates the advantages of the proposed architecture’s ability to produce feature-rich classification with average accuracy of 97.78%, 96.83%, and 94.26% for the first (binary) stage, second (three-class) stage, and all-at-once (four-class) classification, respectively, using cross-validation experiments using the first dataset. In the second dataset, our system showed an overall accuracy, sensitivity, and specificity of 99.69%, 99.71%, and 99.87%, respectively. Overall, the tangible advantages of the proposed network for enhanced feature learning might be used in various medical image classification tasks where scale-invariant features are crucial for precise diagnosis.
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spelling pubmed-103765732023-07-29 Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture Akinniyi, Oluwatunmise Rahman, Md Mahmudur Sandhu, Harpal Singh El-Baz, Ayman Khalifa, Fahmi Bioengineering (Basel) Article Accurate noninvasive diagnosis of retinal disorders is required for appropriate treatment or precision medicine. This work proposes a multi-stage classification network built on a multi-scale (pyramidal) feature ensemble architecture for retinal image classification using optical coherence tomography (OCT) images. First, a scale-adaptive neural network is developed to produce multi-scale inputs for feature extraction and ensemble learning. The larger input sizes yield more global information, while the smaller input sizes focus on local details. Then, a feature-rich pyramidal architecture is designed to extract multi-scale features as inputs using DenseNet as the backbone. The advantage of the hierarchical structure is that it allows the system to extract multi-scale, information-rich features for the accurate classification of retinal disorders. Evaluation on two public OCT datasets containing normal and abnormal retinas (e.g., diabetic macular edema (DME), choroidal neovascularization (CNV), age-related macular degeneration (AMD), and Drusen) and comparison against recent networks demonstrates the advantages of the proposed architecture’s ability to produce feature-rich classification with average accuracy of 97.78%, 96.83%, and 94.26% for the first (binary) stage, second (three-class) stage, and all-at-once (four-class) classification, respectively, using cross-validation experiments using the first dataset. In the second dataset, our system showed an overall accuracy, sensitivity, and specificity of 99.69%, 99.71%, and 99.87%, respectively. Overall, the tangible advantages of the proposed network for enhanced feature learning might be used in various medical image classification tasks where scale-invariant features are crucial for precise diagnosis. MDPI 2023-07-10 /pmc/articles/PMC10376573/ /pubmed/37508850 http://dx.doi.org/10.3390/bioengineering10070823 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
Akinniyi, Oluwatunmise
Rahman, Md Mahmudur
Sandhu, Harpal Singh
El-Baz, Ayman
Khalifa, Fahmi
Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture
title Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture
title_full Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture
title_fullStr Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture
title_full_unstemmed Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture
title_short Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture
title_sort multi-stage classification of retinal oct using multi-scale ensemble deep architecture
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10376573/
https://www.ncbi.nlm.nih.gov/pubmed/37508850
http://dx.doi.org/10.3390/bioengineering10070823
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