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MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features

COVID-19 is a respiratory disease that causes infection in both lungs and the upper respiratory tract. The World Health Organization (WHO) has declared it a global pandemic because of its rapid spread across the globe. The most common way for COVID-19 diagnosis is real-time reverse transcription-pol...

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Main Authors: Dey, Arijit, Chattopadhyay, Soham, Pawan Kumar Singh, Ahmadian, Ali, Ferrara, Massimiliano, Senu, Norazak, Sarkar, Ram
Format: Article
Published: Nature Publishing Group 2021
Online Access:http://psasir.upm.edu.my/id/eprint/102251/
https://www.nature.com/articles/s41598-021-02731-z
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spelling my.upm.eprints.1022512023-07-11T04:03:16Z http://psasir.upm.edu.my/id/eprint/102251/ MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features Dey, Arijit Chattopadhyay, Soham Pawan Kumar Singh Ahmadian, Ali Ferrara, Massimiliano Senu, Norazak Sarkar, Ram COVID-19 is a respiratory disease that causes infection in both lungs and the upper respiratory tract. The World Health Organization (WHO) has declared it a global pandemic because of its rapid spread across the globe. The most common way for COVID-19 diagnosis is real-time reverse transcription-polymerase chain reaction (RT-PCR) which takes a significant amount of time to get the result. Computer based medical image analysis is more beneficial for the diagnosis of such disease as it can give better results in less time. Computed Tomography (CT) scans are used to monitor lung diseases including COVID-19. In this work, a hybrid model for COVID-19 detection has developed which has two key stages. In the first stage, we have fine-tuned the parameters of the pre-trained convolutional neural networks (CNNs) to extract some features from the COVID-19 affected lungs. As pre-trained CNNs, we have used two standard CNNs namely, GoogleNet and ResNet18. Then, we have proposed a hybrid meta-heuristic feature selection (FS) algorithm, named as Manta Ray Foraging based Golden Ratio Optimizer (MRFGRO) to select the most significant feature subset. The proposed model is implemented over three publicly available datasets, namely, COVID-CT dataset, SARS-COV-2 dataset, and MOSMED dataset, and attains state-of-the-art classification accuracies of 99.15%, 99.42% and 95.57% respectively. Obtained results confirm that the proposed approach is quite efficient when compared to the local texture descriptors used for COVID-19 detection from chest CT-scan images. Nature Publishing Group 2021-12-15 Article PeerReviewed Dey, Arijit and Chattopadhyay, Soham and Pawan Kumar Singh and Ahmadian, Ali and Ferrara, Massimiliano and Senu, Norazak and Sarkar, Ram (2021) MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features. Scientific Reports, 11 (1). art. no. 24065. pp. 1-15. ISSN 2045-2322 https://www.nature.com/articles/s41598-021-02731-z 10.1038/s41598-021-02731-z
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description COVID-19 is a respiratory disease that causes infection in both lungs and the upper respiratory tract. The World Health Organization (WHO) has declared it a global pandemic because of its rapid spread across the globe. The most common way for COVID-19 diagnosis is real-time reverse transcription-polymerase chain reaction (RT-PCR) which takes a significant amount of time to get the result. Computer based medical image analysis is more beneficial for the diagnosis of such disease as it can give better results in less time. Computed Tomography (CT) scans are used to monitor lung diseases including COVID-19. In this work, a hybrid model for COVID-19 detection has developed which has two key stages. In the first stage, we have fine-tuned the parameters of the pre-trained convolutional neural networks (CNNs) to extract some features from the COVID-19 affected lungs. As pre-trained CNNs, we have used two standard CNNs namely, GoogleNet and ResNet18. Then, we have proposed a hybrid meta-heuristic feature selection (FS) algorithm, named as Manta Ray Foraging based Golden Ratio Optimizer (MRFGRO) to select the most significant feature subset. The proposed model is implemented over three publicly available datasets, namely, COVID-CT dataset, SARS-COV-2 dataset, and MOSMED dataset, and attains state-of-the-art classification accuracies of 99.15%, 99.42% and 95.57% respectively. Obtained results confirm that the proposed approach is quite efficient when compared to the local texture descriptors used for COVID-19 detection from chest CT-scan images.
format Article
author Dey, Arijit
Chattopadhyay, Soham
Pawan Kumar Singh
Ahmadian, Ali
Ferrara, Massimiliano
Senu, Norazak
Sarkar, Ram
spellingShingle Dey, Arijit
Chattopadhyay, Soham
Pawan Kumar Singh
Ahmadian, Ali
Ferrara, Massimiliano
Senu, Norazak
Sarkar, Ram
MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features
author_facet Dey, Arijit
Chattopadhyay, Soham
Pawan Kumar Singh
Ahmadian, Ali
Ferrara, Massimiliano
Senu, Norazak
Sarkar, Ram
author_sort Dey, Arijit
title MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features
title_short MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features
title_full MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features
title_fullStr MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features
title_full_unstemmed MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features
title_sort mrfgro: a hybrid meta-heuristic feature selection method for screening covid-19 using deep features
publisher Nature Publishing Group
publishDate 2021
url http://psasir.upm.edu.my/id/eprint/102251/
https://www.nature.com/articles/s41598-021-02731-z
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score 13.211869

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