COVID-19 detection from chest X-rays using transfer learning with deep convolutional neural networks

Several well-known pretrained deep convolutional neural network models were evaluated on their ability to detect COVID-19 from chest X-ray images, following a transfer learning approach. The retrained models were tested on two different datasets containing COVID-19, normal, viral, and bacterial pneu...

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Autores principales: Naronglerdrit, Prasitthichai, Mporas, Iosif, Sheikh-Akbari, Akbar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8137676/
http://dx.doi.org/10.1016/B978-0-12-824536-1.00031-9
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author Naronglerdrit, Prasitthichai
Mporas, Iosif
Sheikh-Akbari, Akbar
author_facet Naronglerdrit, Prasitthichai
Mporas, Iosif
Sheikh-Akbari, Akbar
author_sort Naronglerdrit, Prasitthichai
collection PubMed
description Several well-known pretrained deep convolutional neural network models were evaluated on their ability to detect COVID-19 from chest X-ray images, following a transfer learning approach. The retrained models were tested on two different datasets containing COVID-19, normal, viral, and bacterial pneumonia cases. The best performing models among the evaluated ones were the MobileNet, DenseNet, and ResNet after transfer learning retraining with top performing classification accuracies varying from 96.76% to 100%, thus indicating the potential of detecting the new coronavirus from X-ray images.
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spelling pubmed-81376762021-05-21 COVID-19 detection from chest X-rays using transfer learning with deep convolutional neural networks Naronglerdrit, Prasitthichai Mporas, Iosif Sheikh-Akbari, Akbar Data Science for COVID-19 Article Several well-known pretrained deep convolutional neural network models were evaluated on their ability to detect COVID-19 from chest X-ray images, following a transfer learning approach. The retrained models were tested on two different datasets containing COVID-19, normal, viral, and bacterial pneumonia cases. The best performing models among the evaluated ones were the MobileNet, DenseNet, and ResNet after transfer learning retraining with top performing classification accuracies varying from 96.76% to 100%, thus indicating the potential of detecting the new coronavirus from X-ray images. 2021 2021-05-21 /pmc/articles/PMC8137676/ http://dx.doi.org/10.1016/B978-0-12-824536-1.00031-9 Text en Copyright © 2021 Elsevier Inc. All rights reserved. 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
Naronglerdrit, Prasitthichai
Mporas, Iosif
Sheikh-Akbari, Akbar
COVID-19 detection from chest X-rays using transfer learning with deep convolutional neural networks
title COVID-19 detection from chest X-rays using transfer learning with deep convolutional neural networks
title_full COVID-19 detection from chest X-rays using transfer learning with deep convolutional neural networks
title_fullStr COVID-19 detection from chest X-rays using transfer learning with deep convolutional neural networks
title_full_unstemmed COVID-19 detection from chest X-rays using transfer learning with deep convolutional neural networks
title_short COVID-19 detection from chest X-rays using transfer learning with deep convolutional neural networks
title_sort covid-19 detection from chest x-rays using transfer learning with deep convolutional neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8137676/
http://dx.doi.org/10.1016/B978-0-12-824536-1.00031-9
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AT sheikhakbariakbar covid19detectionfromchestxraysusingtransferlearningwithdeepconvolutionalneuralnetworks

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