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A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain

With the global spread of the COVID-19 epidemic, a reliable method is required for identifying COVID-19 victims. The biggest issue in detecting the virus is a lack of testing kits that are both reliable and affordable. Due to the virus's rapid dissemination, medical professionals have trouble f...

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Autores principales: Heidari, Arash, Toumaj, Shiva, Navimipour, Nima Jafari, Unal, Mehmet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Published by Elsevier Ltd. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8958272/
https://www.ncbi.nlm.nih.gov/pubmed/35366470
http://dx.doi.org/10.1016/j.compbiomed.2022.105461
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author Heidari, Arash
Toumaj, Shiva
Navimipour, Nima Jafari
Unal, Mehmet
author_facet Heidari, Arash
Toumaj, Shiva
Navimipour, Nima Jafari
Unal, Mehmet
author_sort Heidari, Arash
collection PubMed
description With the global spread of the COVID-19 epidemic, a reliable method is required for identifying COVID-19 victims. The biggest issue in detecting the virus is a lack of testing kits that are both reliable and affordable. Due to the virus's rapid dissemination, medical professionals have trouble finding positive patients. However, the next real-life issue is sharing data with hospitals around the world while considering the organizations' privacy concerns. The primary worries for training a global Deep Learning (DL) model are creating a collaborative platform and personal confidentiality. Another challenge is exchanging data with health care institutions while protecting the organizations' confidentiality. The primary concerns for training a universal DL model are creating a collaborative platform and preserving privacy. This paper provides a model that receives a small quantity of data from various sources, like organizations or sections of hospitals, and trains a global DL model utilizing blockchain-based Convolutional Neural Networks (CNNs). In addition, we use the Transfer Learning (TL) technique to initialize layers rather than initialize randomly and discover which layers should be removed before selection. Besides, the blockchain system verifies the data, and the DL method trains the model globally while keeping the institution's confidentiality. Furthermore, we gather the actual and novel COVID-19 patients. Finally, we run extensive experiments utilizing Python and its libraries, such as Scikit-Learn and TensorFlow, to assess the proposed method. We evaluated works using five different datasets, including Boukan Dr. Shahid Gholipour hospital, Tabriz Emam Reza hospital, Mahabad Emam Khomeini hospital, Maragheh Dr.Beheshti hospital, and Miandoab Abbasi hospital datasets, and our technique outperform state-of-the-art methods on average in terms of precision (2.7%), recall (3.1%), F1 (2.9%), and accuracy (2.8%).
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spelling pubmed-89582722022-03-28 A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain Heidari, Arash Toumaj, Shiva Navimipour, Nima Jafari Unal, Mehmet Comput Biol Med Article With the global spread of the COVID-19 epidemic, a reliable method is required for identifying COVID-19 victims. The biggest issue in detecting the virus is a lack of testing kits that are both reliable and affordable. Due to the virus's rapid dissemination, medical professionals have trouble finding positive patients. However, the next real-life issue is sharing data with hospitals around the world while considering the organizations' privacy concerns. The primary worries for training a global Deep Learning (DL) model are creating a collaborative platform and personal confidentiality. Another challenge is exchanging data with health care institutions while protecting the organizations' confidentiality. The primary concerns for training a universal DL model are creating a collaborative platform and preserving privacy. This paper provides a model that receives a small quantity of data from various sources, like organizations or sections of hospitals, and trains a global DL model utilizing blockchain-based Convolutional Neural Networks (CNNs). In addition, we use the Transfer Learning (TL) technique to initialize layers rather than initialize randomly and discover which layers should be removed before selection. Besides, the blockchain system verifies the data, and the DL method trains the model globally while keeping the institution's confidentiality. Furthermore, we gather the actual and novel COVID-19 patients. Finally, we run extensive experiments utilizing Python and its libraries, such as Scikit-Learn and TensorFlow, to assess the proposed method. We evaluated works using five different datasets, including Boukan Dr. Shahid Gholipour hospital, Tabriz Emam Reza hospital, Mahabad Emam Khomeini hospital, Maragheh Dr.Beheshti hospital, and Miandoab Abbasi hospital datasets, and our technique outperform state-of-the-art methods on average in terms of precision (2.7%), recall (3.1%), F1 (2.9%), and accuracy (2.8%). Published by Elsevier Ltd. 2022-06 2022-03-28 /pmc/articles/PMC8958272/ /pubmed/35366470 http://dx.doi.org/10.1016/j.compbiomed.2022.105461 Text en © 2022 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
Heidari, Arash
Toumaj, Shiva
Navimipour, Nima Jafari
Unal, Mehmet
A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain
title A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain
title_full A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain
title_fullStr A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain
title_full_unstemmed A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain
title_short A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain
title_sort privacy-aware method for covid-19 detection in chest ct images using lightweight deep conventional neural network and blockchain
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8958272/
https://www.ncbi.nlm.nih.gov/pubmed/35366470
http://dx.doi.org/10.1016/j.compbiomed.2022.105461
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