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Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks
In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7118364/ https://www.ncbi.nlm.nih.gov/pubmed/32524445 http://dx.doi.org/10.1007/s13246-020-00865-4 |
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author | Apostolopoulos, Ioannis D. Mpesiana, Tzani A. |
author_facet | Apostolopoulos, Ioannis D. Mpesiana, Tzani A. |
author_sort | Apostolopoulos, Ioannis D. |
collection | PubMed |
description | In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural network architectures proposed over the recent years for medical image classification. Specifically, the procedure called Transfer Learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The datasets utilized in this experiment are two. Firstly, a collection of 1427 X-ray images including 224 images with confirmed Covid-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 images of normal conditions. Secondly, a dataset including 224 images with confirmed Covid-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions. The data was collected from the available X-ray images on public medical repositories. The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively. Since by now, all diagnostic tests show failure rates such as to raise concerns, the probability of incorporating X-rays into the diagnosis of the disease could be assessed by the medical community, based on the findings, while more research to evaluate the X-ray approach from different aspects may be conducted. |
format | Online Article Text |
id | pubmed-7118364 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-71183642020-04-03 Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks Apostolopoulos, Ioannis D. Mpesiana, Tzani A. Phys Eng Sci Med Scientific Paper In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural network architectures proposed over the recent years for medical image classification. Specifically, the procedure called Transfer Learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The datasets utilized in this experiment are two. Firstly, a collection of 1427 X-ray images including 224 images with confirmed Covid-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 images of normal conditions. Secondly, a dataset including 224 images with confirmed Covid-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions. The data was collected from the available X-ray images on public medical repositories. The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively. Since by now, all diagnostic tests show failure rates such as to raise concerns, the probability of incorporating X-rays into the diagnosis of the disease could be assessed by the medical community, based on the findings, while more research to evaluate the X-ray approach from different aspects may be conducted. Springer International Publishing 2020-04-03 2020 /pmc/articles/PMC7118364/ /pubmed/32524445 http://dx.doi.org/10.1007/s13246-020-00865-4 Text en © Australasian College of Physical Scientists and Engineers in Medicine 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Scientific Paper Apostolopoulos, Ioannis D. Mpesiana, Tzani A. Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks |
title | Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks |
title_full | Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks |
title_fullStr | Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks |
title_full_unstemmed | Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks |
title_short | Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks |
title_sort | covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks |
topic | Scientific Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7118364/ https://www.ncbi.nlm.nih.gov/pubmed/32524445 http://dx.doi.org/10.1007/s13246-020-00865-4 |
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