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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...

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Autores principales: Hassan, Esraa, Elmougy, Samir, Ibraheem, Mai R., Hossain, M. Shamim, AlMutib, Khalid, Ghoneim, Ahmed, AlQahtani, Salman A., Talaat, Fatma M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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.
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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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