Hybrid deep learning models for effective COVID-19 diagnosis with chest x-rays

The survey on COVID-19 test kits RT-PCR (reverse transcription-polymerase chain reaction) concludes the hit rate of diagnosis and detection is degrading. Manufacturing these RT-PCR kits is very expensive and time-consuming. This work proposed an efficient way for COVID detection using a hybrid convo...

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Main Authors: Maheswari, R., Rao, P.S., Azath, H., Asirvadam, V.S.
Format: Book
Published: IGI Global 2023
Online Access:http://scholars.utp.edu.my/id/eprint/37684/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151470195&doi=10.4018%2f978-1-6684-6523-3.ch005&partnerID=40&md5=bbb4235a7e39fd4b54ba55651cb702c2
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Institution: Universiti Teknologi Petronas
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spelling oai:scholars.utp.edu.my:376842023-10-17T03:10:07Z http://scholars.utp.edu.my/id/eprint/37684/ Hybrid deep learning models for effective COVID-19 diagnosis with chest x-rays Maheswari, R. Rao, P.S. Azath, H. Asirvadam, V.S. The survey on COVID-19 test kits RT-PCR (reverse transcription-polymerase chain reaction) concludes the hit rate of diagnosis and detection is degrading. Manufacturing these RT-PCR kits is very expensive and time-consuming. This work proposed an efficient way for COVID detection using a hybrid convolutional neural network (HCNN) through chest x-rays image analysis. It aids to differentiate non-COVID patient and COVID patients. It makes the medical practitioner to take appropriate treatment and measures. The results outperformed the custom blood and saliva-based RT-PCR test results. A few examinations were carried out over chest X-ray images utilizing ConvNets that produce better accuracy for the recognition of COVID-19. When considering the number of images in the database and the COVID discovery season (testing time = 0.03 s/image), the design reduced the computational expenditure. With mean ROC AUC scores 96.51 & 96.33, the CNN with minimised convolutional and fully connected layers detects COVID-19 images inside the two-class COVID/Normal and COVID/Pneumonia orders. © 2023, IGI Global. All rights reserved. IGI Global 2023 Book NonPeerReviewed Maheswari, R. and Rao, P.S. and Azath, H. and Asirvadam, V.S. (2023) Hybrid deep learning models for effective COVID-19 diagnosis with chest x-rays. IGI Global, pp. 98-123. ISBN 9781668465257; 166846523X; 9781668465233 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151470195&doi=10.4018%2f978-1-6684-6523-3.ch005&partnerID=40&md5=bbb4235a7e39fd4b54ba55651cb702c2 10.4018/978-1-6684-6523-3.ch005 10.4018/978-1-6684-6523-3.ch005 10.4018/978-1-6684-6523-3.ch005
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description The survey on COVID-19 test kits RT-PCR (reverse transcription-polymerase chain reaction) concludes the hit rate of diagnosis and detection is degrading. Manufacturing these RT-PCR kits is very expensive and time-consuming. This work proposed an efficient way for COVID detection using a hybrid convolutional neural network (HCNN) through chest x-rays image analysis. It aids to differentiate non-COVID patient and COVID patients. It makes the medical practitioner to take appropriate treatment and measures. The results outperformed the custom blood and saliva-based RT-PCR test results. A few examinations were carried out over chest X-ray images utilizing ConvNets that produce better accuracy for the recognition of COVID-19. When considering the number of images in the database and the COVID discovery season (testing time = 0.03 s/image), the design reduced the computational expenditure. With mean ROC AUC scores 96.51 & 96.33, the CNN with minimised convolutional and fully connected layers detects COVID-19 images inside the two-class COVID/Normal and COVID/Pneumonia orders. © 2023, IGI Global. All rights reserved.
format Book
author Maheswari, R.
Rao, P.S.
Azath, H.
Asirvadam, V.S.
spellingShingle Maheswari, R.
Rao, P.S.
Azath, H.
Asirvadam, V.S.
Hybrid deep learning models for effective COVID-19 diagnosis with chest x-rays
author_facet Maheswari, R.
Rao, P.S.
Azath, H.
Asirvadam, V.S.
author_sort Maheswari, R.
title Hybrid deep learning models for effective COVID-19 diagnosis with chest x-rays
title_short Hybrid deep learning models for effective COVID-19 diagnosis with chest x-rays
title_full Hybrid deep learning models for effective COVID-19 diagnosis with chest x-rays
title_fullStr Hybrid deep learning models for effective COVID-19 diagnosis with chest x-rays
title_full_unstemmed Hybrid deep learning models for effective COVID-19 diagnosis with chest x-rays
title_sort hybrid deep learning models for effective covid-19 diagnosis with chest x-rays
publisher IGI Global
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/37684/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151470195&doi=10.4018%2f978-1-6684-6523-3.ch005&partnerID=40&md5=bbb4235a7e39fd4b54ba55651cb702c2
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