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Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images
Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. The condition has a great effect on the specificity of diagnosis, the monitoring of many physiological and pathological procedures, and the response and evaluation of therap...
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/PMC10301292/ https://www.ncbi.nlm.nih.gov/pubmed/37420558 http://dx.doi.org/10.3390/s23125393 |
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author | Hassan, Esraa Elmougy, Samir Ibraheem, Mai R. Hossain, M. Shamim AlMutib, Khalid Ghoneim, Ahmed AlQahtani, Salman A. Talaat, Fatma M. |
author_facet | Hassan, Esraa Elmougy, Samir Ibraheem, Mai R. Hossain, M. Shamim AlMutib, Khalid Ghoneim, Ahmed AlQahtani, Salman A. Talaat, Fatma M. |
author_sort | Hassan, Esraa |
collection | PubMed |
description | Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. The condition has a great effect on the specificity of diagnosis, the monitoring of many physiological and pathological procedures, and the response and evaluation of therapeutic effectiveness in various fields of clinical practices, including primary eye diseases and systemic diseases such as diabetes. Therefore, precise diagnosis, classification, and automated image analysis models are crucial. In this paper, we propose an enhanced optical coherence tomography (EOCT) model to classify retinal OCT based on modified ResNet (50) and random forest algorithms, which are used in the proposed study’s training strategy to enhance performance. The Adam optimizer is applied during the training process to increase the efficiency of the ResNet (50) model compared with the common pre-trained models, such as spatial separable convolutions and visual geometry group (VGG) (16). The experimentation results show that the sensitivity, specificity, precision, negative predictive value, false discovery rate, false negative rate accuracy, and Matthew’s correlation coefficient are 0.9836, 0.9615, 0.9740, 0.9756, 0.0385, 0.0260, 0.0164, 0.9747, 0.9788, and 0.9474, respectively. |
format | Online Article Text |
id | pubmed-10301292 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103012922023-06-29 Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images Hassan, Esraa Elmougy, Samir Ibraheem, Mai R. Hossain, M. Shamim AlMutib, Khalid Ghoneim, Ahmed AlQahtani, Salman A. Talaat, Fatma M. Sensors (Basel) Article Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. The condition has a great effect on the specificity of diagnosis, the monitoring of many physiological and pathological procedures, and the response and evaluation of therapeutic effectiveness in various fields of clinical practices, including primary eye diseases and systemic diseases such as diabetes. Therefore, precise diagnosis, classification, and automated image analysis models are crucial. In this paper, we propose an enhanced optical coherence tomography (EOCT) model to classify retinal OCT based on modified ResNet (50) and random forest algorithms, which are used in the proposed study’s training strategy to enhance performance. The Adam optimizer is applied during the training process to increase the efficiency of the ResNet (50) model compared with the common pre-trained models, such as spatial separable convolutions and visual geometry group (VGG) (16). The experimentation results show that the sensitivity, specificity, precision, negative predictive value, false discovery rate, false negative rate accuracy, and Matthew’s correlation coefficient are 0.9836, 0.9615, 0.9740, 0.9756, 0.0385, 0.0260, 0.0164, 0.9747, 0.9788, and 0.9474, respectively. MDPI 2023-06-07 /pmc/articles/PMC10301292/ /pubmed/37420558 http://dx.doi.org/10.3390/s23125393 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 Hassan, Esraa Elmougy, Samir Ibraheem, Mai R. Hossain, M. Shamim AlMutib, Khalid Ghoneim, Ahmed AlQahtani, Salman A. Talaat, Fatma M. Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images |
title | Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images |
title_full | Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images |
title_fullStr | Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images |
title_full_unstemmed | Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images |
title_short | Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images |
title_sort | enhanced deep learning model for classification of retinal optical coherence tomography images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301292/ https://www.ncbi.nlm.nih.gov/pubmed/37420558 http://dx.doi.org/10.3390/s23125393 |
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