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...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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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. |
format | Online Article Text |
id | pubmed-9249743 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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