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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Published by Elsevier Ltd.
2022
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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%). |
format | Online Article Text |
id | pubmed-8958272 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
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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