Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images

COVID-19 is a new disease, caused by the novel coronavirus SARS-CoV-2, that was firstly delineated in humans in 2019. Coronaviruses cause a range of illness in patients varying from common cold to advanced respiratory syndromes such as Severe Acute Respiratory Syndrome (SARS-CoV) and Middle East Res...

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Veröffentlicht in:Mathematical problems in engineering Jg. 2021; S. 1 - 9
Hauptverfasser: Shukla, Prashant Kumar, Sandhu, Jasminder Kaur, Ahirwar, Anamika, Ghai, Deepika, Maheshwary, Priti, Shukla, Piyush Kumar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Hindawi 25.02.2021
John Wiley & Sons, Inc
Schlagworte:
Artificial neural networks
Chest
Coronaviruses
COVID-19
COVID-19 diagnostic tests
Datasets
Deep learning
Genetic algorithms
Identification
Machine learning
Mathematical problems
Medical imaging
Multiple objective analysis
Neural networks
Optimization
Respiratory diseases
Severe acute respiratory syndrome
Viral diseases
ISSN:1024-123X, 1563-5147
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Zusammenfassung:COVID-19 is a new disease, caused by the novel coronavirus SARS-CoV-2, that was firstly delineated in humans in 2019. Coronaviruses cause a range of illness in patients varying from common cold to advanced respiratory syndromes such as Severe Acute Respiratory Syndrome (SARS-CoV) and Middle East Respiratory Syndrome (MERS-CoV). The SARS-CoV-2 outbreak has resulted in a global pandemic, and its transmission is increasing at a rapid rate. Diagnostic testing and approaches provide a valuable tool for doctors and support them with the screening process. Automatic COVID-19 identification in chest X-ray images can be useful to test for COVID-19 infection at a good speed. Therefore, in this paper, a framework is designed by using Convolutional Neural Networks (CNN) to diagnose COVID-19 patients using chest X-ray images. A pretrained GoogLeNet is utilized for implementing the transfer learning (i.e., by replacing some sets of final network CNN layers). 20-fold cross-validation is considered to overcome the overfitting quandary. Finally, the multiobjective genetic algorithm is considered to tune the hyperparameters of the proposed COVID-19 identification in chest X-ray images. Extensive experiments show that the proposed COVID-19 identification model obtains remarkably better results and may be utilized for real-time testing of patients.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2021/7804540