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An approach to the classification of COVID-19 based on CT scans using convolutional features and genetic algorithms
COVID-19 is a respiratory disease that, as of July 15th, 2021, has infected more than 187 million people worldwide and is responsible for more than 4 million deaths. An accurate diagnosis of COVID-19 is essential for the treatment and control of the disease. The use of computed tomography (CT) has s...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8342871/ https://www.ncbi.nlm.nih.gov/pubmed/34388465 http://dx.doi.org/10.1016/j.compbiomed.2021.104744 |
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author | Carvalho, Edson D. Silva, Romuere R.V. Araújo, Flávio H.D. Rabelo, Ricardo de A.L. de Carvalho Filho, Antônio Oseas |
author_facet | Carvalho, Edson D. Silva, Romuere R.V. Araújo, Flávio H.D. Rabelo, Ricardo de A.L. de Carvalho Filho, Antônio Oseas |
author_sort | Carvalho, Edson D. |
collection | PubMed |
description | COVID-19 is a respiratory disease that, as of July 15th, 2021, has infected more than 187 million people worldwide and is responsible for more than 4 million deaths. An accurate diagnosis of COVID-19 is essential for the treatment and control of the disease. The use of computed tomography (CT) has shown to be promising for evaluating patients suspected of COVID-19 infection. The analysis of a CT examination is complex, and requires attention from a specialist. This paper presents a methodology for detecting COVID-19 from CT images. We first propose a convolutional neural network architecture to extract features from CT images, and then optimize the hyperparameters of the network using a tree Parzen estimator to choose the best parameters. Following this, we apply a selection of features using a genetic algorithm. Finally, classification is performed using four classifiers with different approaches. The proposed methodology achieved an accuracy of 0.997, a kappa index of 0.995, an AUROC of 0.997, and an AUPRC of 0.997 on the SARS-CoV-2 CT-Scan dataset, and an accuracy of 0.987, a kappa index of 0.975, an AUROC of 0.989, and an AUPRC of 0.987 on the COVID-CT dataset, using our CNN after optimization of the hyperparameters, the selection of features and the multi-layer perceptron classifier. Compared with pretrained CNNs and related state-of-the-art works, the results achieved by the proposed methodology were superior. Our results show that the proposed method can assist specialists in screening and can aid in diagnosing patients with suspected COVID-19. |
format | Online Article Text |
id | pubmed-8342871 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83428712021-08-06 An approach to the classification of COVID-19 based on CT scans using convolutional features and genetic algorithms Carvalho, Edson D. Silva, Romuere R.V. Araújo, Flávio H.D. Rabelo, Ricardo de A.L. de Carvalho Filho, Antônio Oseas Comput Biol Med Article COVID-19 is a respiratory disease that, as of July 15th, 2021, has infected more than 187 million people worldwide and is responsible for more than 4 million deaths. An accurate diagnosis of COVID-19 is essential for the treatment and control of the disease. The use of computed tomography (CT) has shown to be promising for evaluating patients suspected of COVID-19 infection. The analysis of a CT examination is complex, and requires attention from a specialist. This paper presents a methodology for detecting COVID-19 from CT images. We first propose a convolutional neural network architecture to extract features from CT images, and then optimize the hyperparameters of the network using a tree Parzen estimator to choose the best parameters. Following this, we apply a selection of features using a genetic algorithm. Finally, classification is performed using four classifiers with different approaches. The proposed methodology achieved an accuracy of 0.997, a kappa index of 0.995, an AUROC of 0.997, and an AUPRC of 0.997 on the SARS-CoV-2 CT-Scan dataset, and an accuracy of 0.987, a kappa index of 0.975, an AUROC of 0.989, and an AUPRC of 0.987 on the COVID-CT dataset, using our CNN after optimization of the hyperparameters, the selection of features and the multi-layer perceptron classifier. Compared with pretrained CNNs and related state-of-the-art works, the results achieved by the proposed methodology were superior. Our results show that the proposed method can assist specialists in screening and can aid in diagnosing patients with suspected COVID-19. Elsevier Ltd. 2021-09 2021-08-06 /pmc/articles/PMC8342871/ /pubmed/34388465 http://dx.doi.org/10.1016/j.compbiomed.2021.104744 Text en © 2021 Elsevier Ltd. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Carvalho, Edson D. Silva, Romuere R.V. Araújo, Flávio H.D. Rabelo, Ricardo de A.L. de Carvalho Filho, Antônio Oseas An approach to the classification of COVID-19 based on CT scans using convolutional features and genetic algorithms |
title | An approach to the classification of COVID-19 based on CT scans using convolutional features and genetic algorithms |
title_full | An approach to the classification of COVID-19 based on CT scans using convolutional features and genetic algorithms |
title_fullStr | An approach to the classification of COVID-19 based on CT scans using convolutional features and genetic algorithms |
title_full_unstemmed | An approach to the classification of COVID-19 based on CT scans using convolutional features and genetic algorithms |
title_short | An approach to the classification of COVID-19 based on CT scans using convolutional features and genetic algorithms |
title_sort | approach to the classification of covid-19 based on ct scans using convolutional features and genetic algorithms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8342871/ https://www.ncbi.nlm.nih.gov/pubmed/34388465 http://dx.doi.org/10.1016/j.compbiomed.2021.104744 |
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