COVID-19 detection using federated machine learning
Open accessAbstract
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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Article [32] achieved an accuracy of 90.61% and [27] achieved 90%.
“Article [32] and [27] achieved an accuracy of 90.61% and 90% respectively.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [32]
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- Authors
- Abdul Salam, M., Taha, S., & Ramadan, M.
- Affiliations
- Cairo University (EG)
- Egyptian e-Learning University (EG)
- Corresponding author
- Ramadan, Mohamed
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2021-06-08
- Volume
- 16
- Issue
- 6
- Pages
- e0252573
- ISSN
- 1932-6203 (ISSN-L)
- 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
- gold
- doi.org
- 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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