Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images | Zendy

Manjit Kaur | Zendy; Vijay Kumar | Zendy; Vaishali Yadav | Zendy; Dilbag Singh | Zendy; Naresh Kumar | Zendy; Nripendra Narayan Das | Zendy

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Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images

Author(s) -

Manjit Kaur,

Vijay Kumar,

Vaishali Yadav,

Dilbag Singh,

Naresh Kumar,

Nripendra Narayan Das

Publication year - 2021

Publication title -

journal of healthcare engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.509

H-Index - 29

eISSN - 2040-2309

pISSN - 2040-2295

DOI - 10.1155/2021/8829829

Subject(s) - overfitting , hyperparameter , computer science , covid-19 , artificial intelligence , deep learning , metaheuristic , convolutional neural network , pareto principle , machine learning , feature extraction , feature (linguistics) , pattern recognition (psychology) , artificial neural network , medicine , mathematics , statistics , disease , pathology , infectious disease (medical specialty) , linguistics , philosophy

COVID-19 has affected the whole world drastically. A huge number of people have lost their lives due to this pandemic. Early detection of COVID-19 infection is helpful for treatment and quarantine. Therefore, many researchers have designed a deep learning model for the early diagnosis of COVID-19-infected patients. However, deep learning models suffer from overfitting and hyperparameter-tuning issues. To overcome these issues, in this paper, a metaheuristic-based deep COVID-19 screening model is proposed for X-ray images. The modified AlexNet architecture is used for feature extraction and classification of the input images. Strength Pareto evolutionary algorithm-II (SPEA-II) is used to tune the hyperparameters of modified AlexNet. The proposed model is tested on a four-class (i.e., COVID-19, tuberculosis, pneumonia, or healthy) dataset. Finally, the comparisons are drawn among the existing and the proposed models.

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