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Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays

The year 2020 was characterized by the COVID-19 pandemic that has caused, by the end of March 2021, more than 2.5 million deaths worldwide. Since the beginning, besides the laboratory test, used as the gold standard, many applications have been applying deep learning algorithms to chest X-ray images...

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Autores principales: Guarrasi, Valerio, D’Amico, Natascha Claudia, Sicilia, Rosa, Cordelli, Ermanno, Soda, Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Authors. Published by Elsevier Ltd. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8351284/
https://www.ncbi.nlm.nih.gov/pubmed/34393277
http://dx.doi.org/10.1016/j.patcog.2021.108242
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author Guarrasi, Valerio
D’Amico, Natascha Claudia
Sicilia, Rosa
Cordelli, Ermanno
Soda, Paolo
author_facet Guarrasi, Valerio
D’Amico, Natascha Claudia
Sicilia, Rosa
Cordelli, Ermanno
Soda, Paolo
author_sort Guarrasi, Valerio
collection PubMed
description The year 2020 was characterized by the COVID-19 pandemic that has caused, by the end of March 2021, more than 2.5 million deaths worldwide. Since the beginning, besides the laboratory test, used as the gold standard, many applications have been applying deep learning algorithms to chest X-ray images to recognize COVID-19 infected patients. In this context, we found out that convolutional neural networks perform well on a single dataset but struggle to generalize to other data sources. To overcome this limitation, we propose a late fusion approach where we combine the outputs of several state-of-the-art CNNs, introducing a novel method that allows us to construct an optimum ensemble determining which and how many base learners should be aggregated. This choice is driven by a two-objective function that maximizes, on a validation set, the accuracy and the diversity of the ensemble itself. A wide set of experiments on several publicly available datasets, accounting for more than 92,000 images, shows that the proposed approach provides average recognition rates up to 93.54% when tested on external datasets.
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spelling pubmed-83512842021-08-09 Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays Guarrasi, Valerio D’Amico, Natascha Claudia Sicilia, Rosa Cordelli, Ermanno Soda, Paolo Pattern Recognit Article The year 2020 was characterized by the COVID-19 pandemic that has caused, by the end of March 2021, more than 2.5 million deaths worldwide. Since the beginning, besides the laboratory test, used as the gold standard, many applications have been applying deep learning algorithms to chest X-ray images to recognize COVID-19 infected patients. In this context, we found out that convolutional neural networks perform well on a single dataset but struggle to generalize to other data sources. To overcome this limitation, we propose a late fusion approach where we combine the outputs of several state-of-the-art CNNs, introducing a novel method that allows us to construct an optimum ensemble determining which and how many base learners should be aggregated. This choice is driven by a two-objective function that maximizes, on a validation set, the accuracy and the diversity of the ensemble itself. A wide set of experiments on several publicly available datasets, accounting for more than 92,000 images, shows that the proposed approach provides average recognition rates up to 93.54% when tested on external datasets. The Authors. Published by Elsevier Ltd. 2022-01 2021-08-09 /pmc/articles/PMC8351284/ /pubmed/34393277 http://dx.doi.org/10.1016/j.patcog.2021.108242 Text en © 2022 The Authors 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
Guarrasi, Valerio
D’Amico, Natascha Claudia
Sicilia, Rosa
Cordelli, Ermanno
Soda, Paolo
Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays
title Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays
title_full Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays
title_fullStr Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays
title_full_unstemmed Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays
title_short Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays
title_sort pareto optimization of deep networks for covid-19 diagnosis from chest x-rays
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8351284/
https://www.ncbi.nlm.nih.gov/pubmed/34393277
http://dx.doi.org/10.1016/j.patcog.2021.108242
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