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Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches
COVID-19 pandemic has challenged the world science. The international community tries to find, apply, or design novel methods for diagnosis and treatment of COVID-19 patients as soon as possible. Currently, a reliable method for the diagnosis of infected patients is a reverse transcription-polymeras...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Published by Elsevier Ltd.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7388764/ https://www.ncbi.nlm.nih.gov/pubmed/32834651 http://dx.doi.org/10.1016/j.chaos.2020.110170 |
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author | Hassantabar, Shayan Ahmadi, Mohsen Sharifi, Abbas |
author_facet | Hassantabar, Shayan Ahmadi, Mohsen Sharifi, Abbas |
author_sort | Hassantabar, Shayan |
collection | PubMed |
description | COVID-19 pandemic has challenged the world science. The international community tries to find, apply, or design novel methods for diagnosis and treatment of COVID-19 patients as soon as possible. Currently, a reliable method for the diagnosis of infected patients is a reverse transcription-polymerase chain reaction. The method is expensive and time-consuming. Therefore, designing novel methods is important. In this paper, we used three deep learning-based methods for the detection and diagnosis of COVID-19 patients with the use of X-Ray images of lungs. For the diagnosis of the disease, we presented two algorithms include deep neural network (DNN) on the fractal feature of images and convolutional neural network (CNN) methods with the use of the lung images, directly. Results classification shows that the presented CNN architecture with higher accuracy (93.2%) and sensitivity (96.1%) is outperforming than the DNN method with an accuracy of 83.4% and sensitivity of 86%. In the segmentation process, we presented a CNN architecture to find infected tissue in lung images. Results show that the presented method can almost detect infected regions with high accuracy of 83.84%. This finding also can be used to monitor and control patients from infected region growth. |
format | Online Article Text |
id | pubmed-7388764 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73887642020-07-30 Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches Hassantabar, Shayan Ahmadi, Mohsen Sharifi, Abbas Chaos Solitons Fractals Article COVID-19 pandemic has challenged the world science. The international community tries to find, apply, or design novel methods for diagnosis and treatment of COVID-19 patients as soon as possible. Currently, a reliable method for the diagnosis of infected patients is a reverse transcription-polymerase chain reaction. The method is expensive and time-consuming. Therefore, designing novel methods is important. In this paper, we used three deep learning-based methods for the detection and diagnosis of COVID-19 patients with the use of X-Ray images of lungs. For the diagnosis of the disease, we presented two algorithms include deep neural network (DNN) on the fractal feature of images and convolutional neural network (CNN) methods with the use of the lung images, directly. Results classification shows that the presented CNN architecture with higher accuracy (93.2%) and sensitivity (96.1%) is outperforming than the DNN method with an accuracy of 83.4% and sensitivity of 86%. In the segmentation process, we presented a CNN architecture to find infected tissue in lung images. Results show that the presented method can almost detect infected regions with high accuracy of 83.84%. This finding also can be used to monitor and control patients from infected region growth. Published by Elsevier Ltd. 2020-11 2020-07-29 /pmc/articles/PMC7388764/ /pubmed/32834651 http://dx.doi.org/10.1016/j.chaos.2020.110170 Text en © 2020 Published by 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 Hassantabar, Shayan Ahmadi, Mohsen Sharifi, Abbas Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches |
title | Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches |
title_full | Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches |
title_fullStr | Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches |
title_full_unstemmed | Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches |
title_short | Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches |
title_sort | diagnosis and detection of infected tissue of covid-19 patients based on lung x-ray image using convolutional neural network approaches |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7388764/ https://www.ncbi.nlm.nih.gov/pubmed/32834651 http://dx.doi.org/10.1016/j.chaos.2020.110170 |
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