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Performance Analysis of Machine Learning and Deep Learning Architectures on Early Stroke Detection Using Carotid Artery Ultrasound Images

Atherosclerotic plaque deposit in the carotid artery is used as an early estimate to identify the presence of cardiovascular diseases. Ultrasound images of the carotid artery are used to provide the extent of stenosis by examining the intima-media thickness and plaque diameter. A total of 361 images...

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Autores principales: Latha, S., Muthu, P., Lai, Khin Wee, Khalil, Azira, Dhanalakshmi, Samiappan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8830903/
https://www.ncbi.nlm.nih.gov/pubmed/35153728
http://dx.doi.org/10.3389/fnagi.2021.828214
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author Latha, S.
Muthu, P.
Lai, Khin Wee
Khalil, Azira
Dhanalakshmi, Samiappan
author_facet Latha, S.
Muthu, P.
Lai, Khin Wee
Khalil, Azira
Dhanalakshmi, Samiappan
author_sort Latha, S.
collection PubMed
description Atherosclerotic plaque deposit in the carotid artery is used as an early estimate to identify the presence of cardiovascular diseases. Ultrasound images of the carotid artery are used to provide the extent of stenosis by examining the intima-media thickness and plaque diameter. A total of 361 images were classified using machine learning and deep learning approaches to recognize whether the person is symptomatic or asymptomatic. CART decision tree, random forest, and logistic regression machine learning algorithms, convolutional neural network (CNN), Mobilenet, and Capsulenet deep learning algorithms were applied in 202 normal images and 159 images with carotid plaque. Random forest provided a competitive accuracy of 91.41% and Capsulenet transfer learning approach gave 96.7% accuracy in classifying the carotid artery ultrasound image database.
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spelling pubmed-88309032022-02-11 Performance Analysis of Machine Learning and Deep Learning Architectures on Early Stroke Detection Using Carotid Artery Ultrasound Images Latha, S. Muthu, P. Lai, Khin Wee Khalil, Azira Dhanalakshmi, Samiappan Front Aging Neurosci Neuroscience Atherosclerotic plaque deposit in the carotid artery is used as an early estimate to identify the presence of cardiovascular diseases. Ultrasound images of the carotid artery are used to provide the extent of stenosis by examining the intima-media thickness and plaque diameter. A total of 361 images were classified using machine learning and deep learning approaches to recognize whether the person is symptomatic or asymptomatic. CART decision tree, random forest, and logistic regression machine learning algorithms, convolutional neural network (CNN), Mobilenet, and Capsulenet deep learning algorithms were applied in 202 normal images and 159 images with carotid plaque. Random forest provided a competitive accuracy of 91.41% and Capsulenet transfer learning approach gave 96.7% accuracy in classifying the carotid artery ultrasound image database. Frontiers Media S.A. 2022-01-27 /pmc/articles/PMC8830903/ /pubmed/35153728 http://dx.doi.org/10.3389/fnagi.2021.828214 Text en Copyright © 2022 Latha, Muthu, Lai, Khalil and Dhanalakshmi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Latha, S.
Muthu, P.
Lai, Khin Wee
Khalil, Azira
Dhanalakshmi, Samiappan
Performance Analysis of Machine Learning and Deep Learning Architectures on Early Stroke Detection Using Carotid Artery Ultrasound Images
title Performance Analysis of Machine Learning and Deep Learning Architectures on Early Stroke Detection Using Carotid Artery Ultrasound Images
title_full Performance Analysis of Machine Learning and Deep Learning Architectures on Early Stroke Detection Using Carotid Artery Ultrasound Images
title_fullStr Performance Analysis of Machine Learning and Deep Learning Architectures on Early Stroke Detection Using Carotid Artery Ultrasound Images
title_full_unstemmed Performance Analysis of Machine Learning and Deep Learning Architectures on Early Stroke Detection Using Carotid Artery Ultrasound Images
title_short Performance Analysis of Machine Learning and Deep Learning Architectures on Early Stroke Detection Using Carotid Artery Ultrasound Images
title_sort performance analysis of machine learning and deep learning architectures on early stroke detection using carotid artery ultrasound images
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8830903/
https://www.ncbi.nlm.nih.gov/pubmed/35153728
http://dx.doi.org/10.3389/fnagi.2021.828214
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