Bayesian-based optimized deep learning model to detect COVID-19 patients using chest X-ray image data

Coronavirus Disease 2019 (COVID-19) is extremely infectious and rapidly spreading around the globe. As a result, rapid and precise identification of COVID-19 patients is critical. Deep Learning has shown promising performance in a variety of domains and emerged as a key technology in Artificial Inte...

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Bibliographic Details
Published in:Computers in biology and medicine Vol. 142; p. 105213
Main Authors: Loey, Mohamed, El-Sappagh, Shaker, Mirjalili, Seyedali
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.03.2022
Elsevier Limited
Subjects:
Ablation
Accuracy
Algorithms
Artificial Intelligence
Artificial neural networks
Bayes Theorem
Bayesian analysis
Bayesian optimization
Chest
Climate change
Convolutional neural network
Coronaviruses
COVID-19
COVID-19 diagnostic tests
COVID-19 vaccines
Datasets
Deep Learning
Disease transmission
Feature extraction
Humans
Image classification
Immunization
Infections
Internal Medicine
Machine learning
Medical imaging
Neural networks
Object recognition
Optimization
Other
Overpopulation
Patients
Pneumonia
Radiography, Thoracic
SARS-CoV-2
Severe acute respiratory syndrome coronavirus 2
Viral diseases
X-Rays
COVID-19
Deep learning
Bayesian optimization
Convolutional neural network
Optimization
Image classification
ISSN:0010-4825, 1879-0534, 1879-0534
Online Access:Get full text
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Summary:Coronavirus Disease 2019 (COVID-19) is extremely infectious and rapidly spreading around the globe. As a result, rapid and precise identification of COVID-19 patients is critical. Deep Learning has shown promising performance in a variety of domains and emerged as a key technology in Artificial Intelligence. Recent advances in visual recognition are based on image classification and artefacts detection within these images. The purpose of this study is to classify chest X-ray images of COVID-19 artefacts in changed real-world situations. A novel Bayesian optimization-based convolutional neural network (CNN) model is proposed for the recognition of chest X-ray images. The proposed model has two main components. The first one utilizes CNN to extract and learn deep features. The second component is a Bayesian-based optimizer that is used to tune the CNN hyperparameters according to an objective function. The used large-scale and balanced dataset comprises 10,848 images (i.e., 3616 COVID-19, 3616 normal cases, and 3616 Pneumonia). In the first ablation investigation, we compared Bayesian optimization to three distinct ablation scenarios. We used convergence charts and accuracy to compare the three scenarios. We noticed that the Bayesian search-derived optimal architecture achieved 96% accuracy. To assist qualitative researchers, address their research questions in a methodologically sound manner, a comparison of research method and theme analysis methods was provided. The suggested model is shown to be more trustworthy and accurate in real world. •A novel Bayesian optimization-based convolutional neural network model is proposed.•CNN is used to extract and learn deep features.•A Bayesian-based optimizer that is used to tune the CNN hyperparameters.•The proposed algorithm is used for the recognition of chest X-ray images.•The Bayesian search-derived optimal architecture achieved 96% accuracy.
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2022.105213