RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance

Coronary artery disease (CAD) is a prevalent disease with high morbidity and mortality rates. Invasive coronary angiography is the reference standard for diagnosing CAD but is costly and associated with risks. Noninvasive imaging like cardiac magnetic resonance (CMR) facilitates CAD assessment and c...

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Autores principales: Khozeimeh, Fahime, Sharifrazi, Danial, Izadi, Navid Hoseini, Joloudari, Javad Hassannataj, Shoeibi, Afshin, Alizadehsani, Roohallah, Tartibi, Mehrzad, Hussain, Sadiq, Sani, Zahra Alizadeh, Khodatars, Marjane, Sadeghi, Delaram, Khosravi, Abbas, Nahavandi, Saeid, Tan, Ru-San, Acharya, U. Rajendra, Islam, Sheikh Mohammed Shariful
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9249743/
https://www.ncbi.nlm.nih.gov/pubmed/35778476
http://dx.doi.org/10.1038/s41598-022-15374-5
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author Khozeimeh, Fahime
Sharifrazi, Danial
Izadi, Navid Hoseini
Joloudari, Javad Hassannataj
Shoeibi, Afshin
Alizadehsani, Roohallah
Tartibi, Mehrzad
Hussain, Sadiq
Sani, Zahra Alizadeh
Khodatars, Marjane
Sadeghi, Delaram
Khosravi, Abbas
Nahavandi, Saeid
Tan, Ru-San
Acharya, U. Rajendra
Islam, Sheikh Mohammed Shariful
author_facet Khozeimeh, Fahime
Sharifrazi, Danial
Izadi, Navid Hoseini
Joloudari, Javad Hassannataj
Shoeibi, Afshin
Alizadehsani, Roohallah
Tartibi, Mehrzad
Hussain, Sadiq
Sani, Zahra Alizadeh
Khodatars, Marjane
Sadeghi, Delaram
Khosravi, Abbas
Nahavandi, Saeid
Tan, Ru-San
Acharya, U. Rajendra
Islam, Sheikh Mohammed Shariful
author_sort Khozeimeh, Fahime
collection PubMed
description Coronary artery disease (CAD) is a prevalent disease with high morbidity and mortality rates. Invasive coronary angiography is the reference standard for diagnosing CAD but is costly and associated with risks. Noninvasive imaging like cardiac magnetic resonance (CMR) facilitates CAD assessment and can serve as a gatekeeper to downstream invasive testing. Machine learning methods are increasingly applied for automated interpretation of imaging and other clinical results for medical diagnosis. In this study, we proposed a novel CAD detection method based on CMR images by utilizing the feature extraction ability of deep neural networks and combining the features with the aid of a random forest for the very first time. It is necessary to convert image data to numeric features so that they can be used in the nodes of the decision trees. To this end, the predictions of multiple stand-alone convolutional neural networks (CNNs) were considered as input features for the decision trees. The capability of CNNs in representing image data renders our method a generic classification approach applicable to any image dataset. We named our method RF-CNN-F, which stands for Random Forest with CNN Features. We conducted experiments on a large CMR dataset that we have collected and made publicly accessible. Our method achieved excellent accuracy (99.18%) using Adam optimizer compared to a stand-alone CNN trained using fivefold cross validation (93.92%) tested on the same dataset.
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spelling pubmed-92497432022-07-03 RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance Khozeimeh, Fahime Sharifrazi, Danial Izadi, Navid Hoseini Joloudari, Javad Hassannataj Shoeibi, Afshin Alizadehsani, Roohallah Tartibi, Mehrzad Hussain, Sadiq Sani, Zahra Alizadeh Khodatars, Marjane Sadeghi, Delaram Khosravi, Abbas Nahavandi, Saeid Tan, Ru-San Acharya, U. Rajendra Islam, Sheikh Mohammed Shariful Sci Rep Article Coronary artery disease (CAD) is a prevalent disease with high morbidity and mortality rates. Invasive coronary angiography is the reference standard for diagnosing CAD but is costly and associated with risks. Noninvasive imaging like cardiac magnetic resonance (CMR) facilitates CAD assessment and can serve as a gatekeeper to downstream invasive testing. Machine learning methods are increasingly applied for automated interpretation of imaging and other clinical results for medical diagnosis. In this study, we proposed a novel CAD detection method based on CMR images by utilizing the feature extraction ability of deep neural networks and combining the features with the aid of a random forest for the very first time. It is necessary to convert image data to numeric features so that they can be used in the nodes of the decision trees. To this end, the predictions of multiple stand-alone convolutional neural networks (CNNs) were considered as input features for the decision trees. The capability of CNNs in representing image data renders our method a generic classification approach applicable to any image dataset. We named our method RF-CNN-F, which stands for Random Forest with CNN Features. We conducted experiments on a large CMR dataset that we have collected and made publicly accessible. Our method achieved excellent accuracy (99.18%) using Adam optimizer compared to a stand-alone CNN trained using fivefold cross validation (93.92%) tested on the same dataset. Nature Publishing Group UK 2022-07-01 /pmc/articles/PMC9249743/ /pubmed/35778476 http://dx.doi.org/10.1038/s41598-022-15374-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Khozeimeh, Fahime
Sharifrazi, Danial
Izadi, Navid Hoseini
Joloudari, Javad Hassannataj
Shoeibi, Afshin
Alizadehsani, Roohallah
Tartibi, Mehrzad
Hussain, Sadiq
Sani, Zahra Alizadeh
Khodatars, Marjane
Sadeghi, Delaram
Khosravi, Abbas
Nahavandi, Saeid
Tan, Ru-San
Acharya, U. Rajendra
Islam, Sheikh Mohammed Shariful
RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance
title RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance
title_full RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance
title_fullStr RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance
title_full_unstemmed RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance
title_short RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance
title_sort rf-cnn-f: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9249743/
https://www.ncbi.nlm.nih.gov/pubmed/35778476
http://dx.doi.org/10.1038/s41598-022-15374-5
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