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Cited by 109Privacy-Preserving Technologies in DataCOVID-19 diagnosis using AIArtificial Intelligence in Healthcare and Education

The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.

COVID-19 detection using federated machine learning

Open access
Abdul Salam, Mustafa, Taha, Sanaa, Ramadan, Mohamed·2021·PLOS ONE
DOI 10.1371/journal.pone.0252573PMC PMC8186799
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Abstract

The current COVID-19 pandemic threatens human life, health, and productivity. AI plays an essential role in COVID-19 case classification as we can apply machine learning models on COVID-19 case data to predict infectious cases and recovery rates using chest x-ray. Accessing patient’s private data violates patient privacy and traditional machine learning model requires accessing or transferring whole data to train the model. In recent years, there has been increasing interest in federated machine learning, as it provides an effective solution for data privacy, centralized computation, and high computation power. In this paper, we studied the efficacy of federated learning versus traditional learning by developing two machine learning models (a federated learning model and a traditional machine learning model)using Keras and TensorFlow federated, we used a descriptive dataset and chest x-ray (CXR) images from COVID-19 patients. During the model training stage, we tried to identify which factors affect model prediction accuracy and loss like activation function, model optimizer, learning rate, number of rounds, and data Size, we kept recording and plotting the model loss and prediction accuracy per each training round, to identify which factors affect the model performance, and we found that softmax activation function and SGD optimizer give better prediction accuracy and loss, changing the number of rounds and learning rate has slightly effect on model prediction accuracy and prediction loss but increasing the data size did not have any effect on model prediction accuracy and prediction loss. finally, we build a comparison between the proposed models’ loss, accuracy, and performance speed, the results demonstrate that the federated machine learning model has a better prediction accuracy and loss but higher performance time than the traditional machine learning model.

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Crossref · 10.1371/journal.pone.0252573

Cites

39 of 39 references verified0 citation errors · 0 not checkable

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Figures

Fig 1 The global model collects local models updates.
Fig 1 The global model collects local models updates.CC BY · PubMed Central
Fig 2 The proposed federated model for classifying COVID-19 cases from patient’s chest x-ray images.
Fig 2 The proposed federated model for classifying COVID-19 cases from patient’s chest x-ray images.CC BY · PubMed Central
Fig 3 The proposed federated model for classifying COVID-19 cases from patient’s descriptive data.
Fig 3 The proposed federated model for classifying COVID-19 cases from patient’s descriptive data.CC BY · PubMed Central
Fig 4 The proposed traditional model for classifying COVID-19 cases from chest x-ray images.
Fig 4 The proposed traditional model for classifying COVID-19 cases from chest x-ray images.CC BY · PubMed Central
Fig 5 The proposed traditional model for classifying COVID-19 cases from patient’s descriptive dataset.
Fig 5 The proposed traditional model for classifying COVID-19 cases from patient’s descriptive dataset.CC BY · PubMed Central
Fig 6 Model accuracy, loss and time comparison on descriptive patient’s dataset.
Fig 6 Model accuracy, loss and time comparison on descriptive patient’s dataset.CC BY · PubMed Central
Fig 7 Model accuracy, loss and time comparison on patient’s chest x-rays dataset.
Fig 7 Model accuracy, loss and time comparison on patient’s chest x-rays dataset.CC BY · PubMed Central
Fig 8 Model accuracy, loss and time comparison on patient’s chest x-rays dataset.
Fig 8 Model accuracy, loss and time comparison on patient’s chest x-rays dataset.CC BY · PubMed Central
Fig 9 Optimizer loss comparison.
Fig 9 Optimizer loss comparison.CC BY · PubMed Central
Fig 10 Learning rate comparison.
Fig 10 Learning rate comparison.CC BY · PubMed Central
Fig 11 Model loss comparison for 10, 50 round.
Fig 11 Model loss comparison for 10, 50 round.CC BY · PubMed Central
Fig 12 Model accuracy and loss comparison for 500 round.
Fig 12 Model accuracy and loss comparison for 500 round.CC BY · PubMed Central
Fig 13 Model loss comparison for 10, 50 round.
Fig 13 Model loss comparison for 10, 50 round.CC BY · PubMed Central
Fig 14 Model accuracy and loss comparison for 100 times data size.
Fig 14 Model accuracy and loss comparison for 100 times data size.CC BY · PubMed Central

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Research works printed but not linked: 1

  1. Ann Cavoukian, 2009, Privacy by design: The 7 foundational principles, Information and privacy commissioner of Ontario, Canada, 5, 12

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Metadata

Authors
Abdul Salam, M., Taha, S., & Ramadan, M.
Affiliations
  • Cairo University (EG)
  • Egyptian e-Learning University (EG)
Corresponding author
Ramadan, Mohamed
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2021-06-08
Volume
16
Issue
6
Pages
e0252573
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0252573
PMC
PMC8186799
OpenAlex
W3170020805
Cited by
109
References
37
Citations by year
2026: 2 · 2025: 10 · 2024: 19 · 2023: 37 · 2022: 33 · 2021: 8
FWCI
8.53
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Greater South Information System
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
COVID-19 detection, federated learning, chest radiography, patient privacy, model accuracy comparison, hyperparameter effects, softmax activation, SGD optimizer, dataset size
MeSH terms
Machine Learning, COVID-19, Computer Simulation, Humans, Image Processing, Computer-Assisted, Thorax, Tomography, X-Ray Computed, Datasets as Topic

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