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Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images
(1) Background: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a long-established estimation methodology for medical diagnosis using image classification illustrating conditions in coronary artery disease. For these procedures, convolutional neural networks...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9267142/ https://www.ncbi.nlm.nih.gov/pubmed/35807203 http://dx.doi.org/10.3390/jcm11133918 |
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author | Papandrianos, Nikolaos I. Feleki, Anna Papageorgiou, Elpiniki I. Martini, Chiara |
author_facet | Papandrianos, Nikolaos I. Feleki, Anna Papageorgiou, Elpiniki I. Martini, Chiara |
author_sort | Papandrianos, Nikolaos I. |
collection | PubMed |
description | (1) Background: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a long-established estimation methodology for medical diagnosis using image classification illustrating conditions in coronary artery disease. For these procedures, convolutional neural networks have proven to be very beneficial in achieving near-optimal accuracy for the automatic classification of SPECT images. (2) Methods: This research addresses the supervised learning-based ideal observer image classification utilizing an RGB-CNN model in heart images to diagnose CAD. For comparison purposes, we employ VGG-16 and DenseNet-121 pre-trained networks that are indulged in an image dataset representing stress and rest mode heart states acquired by SPECT. In experimentally evaluating the method, we explore a wide repertoire of deep learning network setups in conjunction with various robust evaluation and exploitation metrics. Additionally, to overcome the image dataset cardinality restrictions, we take advantage of the data augmentation technique expanding the set into an adequate number. Further evaluation of the model was performed via 10-fold cross-validation to ensure our model’s reliability. (3) Results: The proposed RGB-CNN model achieved an accuracy of 91.86%, while VGG-16 and DenseNet-121 reached 88.54% and 86.11%, respectively. (4) Conclusions: The abovementioned experiments verify that the newly developed deep learning models may be of great assistance in nuclear medicine and clinical decision-making. |
format | Online Article Text |
id | pubmed-9267142 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92671422022-07-09 Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images Papandrianos, Nikolaos I. Feleki, Anna Papageorgiou, Elpiniki I. Martini, Chiara J Clin Med Article (1) Background: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a long-established estimation methodology for medical diagnosis using image classification illustrating conditions in coronary artery disease. For these procedures, convolutional neural networks have proven to be very beneficial in achieving near-optimal accuracy for the automatic classification of SPECT images. (2) Methods: This research addresses the supervised learning-based ideal observer image classification utilizing an RGB-CNN model in heart images to diagnose CAD. For comparison purposes, we employ VGG-16 and DenseNet-121 pre-trained networks that are indulged in an image dataset representing stress and rest mode heart states acquired by SPECT. In experimentally evaluating the method, we explore a wide repertoire of deep learning network setups in conjunction with various robust evaluation and exploitation metrics. Additionally, to overcome the image dataset cardinality restrictions, we take advantage of the data augmentation technique expanding the set into an adequate number. Further evaluation of the model was performed via 10-fold cross-validation to ensure our model’s reliability. (3) Results: The proposed RGB-CNN model achieved an accuracy of 91.86%, while VGG-16 and DenseNet-121 reached 88.54% and 86.11%, respectively. (4) Conclusions: The abovementioned experiments verify that the newly developed deep learning models may be of great assistance in nuclear medicine and clinical decision-making. MDPI 2022-07-05 /pmc/articles/PMC9267142/ /pubmed/35807203 http://dx.doi.org/10.3390/jcm11133918 Text en © 2022 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 Papandrianos, Nikolaos I. Feleki, Anna Papageorgiou, Elpiniki I. Martini, Chiara Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images |
title | Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images |
title_full | Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images |
title_fullStr | Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images |
title_full_unstemmed | Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images |
title_short | Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images |
title_sort | deep learning-based automated diagnosis for coronary artery disease using spect-mpi images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9267142/ https://www.ncbi.nlm.nih.gov/pubmed/35807203 http://dx.doi.org/10.3390/jcm11133918 |
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