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FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction
With the SARS-CoV-2's exponential growth, intelligent and constructive practice is required to diagnose the COVID-19. The rapid spread of the virus and the shortage of reliable testing models are considered major issues in detecting COVID-19. This problem remains the peak burden for clinicians....
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9247602/ https://www.ncbi.nlm.nih.gov/pubmed/35784262 http://dx.doi.org/10.3389/fpubh.2022.892499 |
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author | Durga, R. Poovammal, E. |
author_facet | Durga, R. Poovammal, E. |
author_sort | Durga, R. |
collection | PubMed |
description | With the SARS-CoV-2's exponential growth, intelligent and constructive practice is required to diagnose the COVID-19. The rapid spread of the virus and the shortage of reliable testing models are considered major issues in detecting COVID-19. This problem remains the peak burden for clinicians. With the advent of artificial intelligence (AI) in image processing, the burden of diagnosing the COVID-19 cases has been reduced to acceptable thresholds. But traditional AI techniques often require centralized data storage and training for the predictive model development which increases the computational complexity. The real-world challenge is to exchange data globally across hospitals while also taking into account of the organizations' privacy concerns. Collaborative model development and privacy protection are critical considerations while training a global deep learning model. To address these challenges, this paper proposes a novel framework based on blockchain and the federated learning model. The federated learning model takes care of reduced complexity, and blockchain helps in distributed data with privacy maintained. More precisely, the proposed federated learning ensembled deep five learning blockchain model (FLED-Block) framework collects the data from the different medical healthcare centers, develops the model with the hybrid capsule learning network, and performs the prediction accurately, while preserving the privacy and shares among authorized persons. Extensive experimentation has been carried out using the lung CT images and compared the performance of the proposed model with the existing VGG-16 and 19, Alexnets, Resnets-50 and 100, Inception V3, Densenets-121, 119, and 150, Mobilenets, SegCaps in terms of accuracy (98.2%), precision (97.3%), recall (96.5%), specificity (33.5%), and F1-score (97%) in predicting the COVID-19 with effectively preserving the privacy of the data among the heterogeneous users. |
format | Online Article Text |
id | pubmed-9247602 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92476022022-07-02 FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction Durga, R. Poovammal, E. Front Public Health Public Health With the SARS-CoV-2's exponential growth, intelligent and constructive practice is required to diagnose the COVID-19. The rapid spread of the virus and the shortage of reliable testing models are considered major issues in detecting COVID-19. This problem remains the peak burden for clinicians. With the advent of artificial intelligence (AI) in image processing, the burden of diagnosing the COVID-19 cases has been reduced to acceptable thresholds. But traditional AI techniques often require centralized data storage and training for the predictive model development which increases the computational complexity. The real-world challenge is to exchange data globally across hospitals while also taking into account of the organizations' privacy concerns. Collaborative model development and privacy protection are critical considerations while training a global deep learning model. To address these challenges, this paper proposes a novel framework based on blockchain and the federated learning model. The federated learning model takes care of reduced complexity, and blockchain helps in distributed data with privacy maintained. More precisely, the proposed federated learning ensembled deep five learning blockchain model (FLED-Block) framework collects the data from the different medical healthcare centers, develops the model with the hybrid capsule learning network, and performs the prediction accurately, while preserving the privacy and shares among authorized persons. Extensive experimentation has been carried out using the lung CT images and compared the performance of the proposed model with the existing VGG-16 and 19, Alexnets, Resnets-50 and 100, Inception V3, Densenets-121, 119, and 150, Mobilenets, SegCaps in terms of accuracy (98.2%), precision (97.3%), recall (96.5%), specificity (33.5%), and F1-score (97%) in predicting the COVID-19 with effectively preserving the privacy of the data among the heterogeneous users. Frontiers Media S.A. 2022-06-17 /pmc/articles/PMC9247602/ /pubmed/35784262 http://dx.doi.org/10.3389/fpubh.2022.892499 Text en Copyright © 2022 Durga and Poovammal. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Durga, R. Poovammal, E. FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction |
title | FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction |
title_full | FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction |
title_fullStr | FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction |
title_full_unstemmed | FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction |
title_short | FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction |
title_sort | fled-block: federated learning ensembled deep learning blockchain model for covid-19 prediction |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9247602/ https://www.ncbi.nlm.nih.gov/pubmed/35784262 http://dx.doi.org/10.3389/fpubh.2022.892499 |
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