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Detecting COVID-19 from Lung Computed Tomography Images: A Swarm Optimised Artificial Neural Network Approach

COVID-19 has affected many people across the globe. Though vaccines are available now, early detection of the disease plays a vital role in the better management of COVID-19 patients. An Artificial Neural Network (ANN) powered Computer Aided Diagnosis (CAD) system can automate the detection pipeline...

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Main Authors: Punitha, S., Stephan, T., Kannan, R., Mahmud, M., Kaiser, M.S., Belhaouari, S.B.
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Online Access:http://scholars.utp.edu.my/id/eprint/34329/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147307735&doi=10.1109%2fACCESS.2023.3236812&partnerID=40&md5=900b2db4aaf9a2ae1f7ca6d69d5b91f9
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spelling oai:scholars.utp.edu.my:343292023-02-17T12:58:30Z http://scholars.utp.edu.my/id/eprint/34329/ Detecting COVID-19 from Lung Computed Tomography Images: A Swarm Optimised Artificial Neural Network Approach Punitha, S. Stephan, T. Kannan, R. Mahmud, M. Kaiser, M.S. Belhaouari, S.B. COVID-19 has affected many people across the globe. Though vaccines are available now, early detection of the disease plays a vital role in the better management of COVID-19 patients. An Artificial Neural Network (ANN) powered Computer Aided Diagnosis (CAD) system can automate the detection pipeline accounting for accurate diagnosis, overcoming the limitations of manual methods. This work proposes a CAD system for COVID-19 that detects and classifies abnormalities in lung CT images using Artificial Bee Colony (ABC) optimised ANN (ABCNN). The proposed ABCNN approach works by segmenting the suspicious regions from the CT images of non-COVID and COVID patients using an ABC optimised region growing process and extracting the texture and intensity features from those suspicious regions. Further, an optimised ANN model whose input features, initial weights and hidden nodes are optimised using ABC optimisation classifies those abnormal regions into COVID and non-COVID classes. The proposed ABCNN approach is evaluated using the lung CT images collected from the public datasets. In comparison to other available techniques, the proposed ABCNN approach achieved a high classification accuracy of 92.37 when evaluated using a set of 470 lung CT images. Author Institute of Electrical and Electronics Engineers Inc. 2023 Article NonPeerReviewed Punitha, S. and Stephan, T. and Kannan, R. and Mahmud, M. and Kaiser, M.S. and Belhaouari, S.B. (2023) Detecting COVID-19 from Lung Computed Tomography Images: A Swarm Optimised Artificial Neural Network Approach. IEEE Access. p. 1. ISSN 21693536 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147307735&doi=10.1109%2fACCESS.2023.3236812&partnerID=40&md5=900b2db4aaf9a2ae1f7ca6d69d5b91f9 10.1109/ACCESS.2023.3236812 10.1109/ACCESS.2023.3236812 10.1109/ACCESS.2023.3236812
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 COVID-19 has affected many people across the globe. Though vaccines are available now, early detection of the disease plays a vital role in the better management of COVID-19 patients. An Artificial Neural Network (ANN) powered Computer Aided Diagnosis (CAD) system can automate the detection pipeline accounting for accurate diagnosis, overcoming the limitations of manual methods. This work proposes a CAD system for COVID-19 that detects and classifies abnormalities in lung CT images using Artificial Bee Colony (ABC) optimised ANN (ABCNN). The proposed ABCNN approach works by segmenting the suspicious regions from the CT images of non-COVID and COVID patients using an ABC optimised region growing process and extracting the texture and intensity features from those suspicious regions. Further, an optimised ANN model whose input features, initial weights and hidden nodes are optimised using ABC optimisation classifies those abnormal regions into COVID and non-COVID classes. The proposed ABCNN approach is evaluated using the lung CT images collected from the public datasets. In comparison to other available techniques, the proposed ABCNN approach achieved a high classification accuracy of 92.37 when evaluated using a set of 470 lung CT images. Author
format Article
author Punitha, S.
Stephan, T.
Kannan, R.
Mahmud, M.
Kaiser, M.S.
Belhaouari, S.B.
spellingShingle Punitha, S.
Stephan, T.
Kannan, R.
Mahmud, M.
Kaiser, M.S.
Belhaouari, S.B.
Detecting COVID-19 from Lung Computed Tomography Images: A Swarm Optimised Artificial Neural Network Approach
author_facet Punitha, S.
Stephan, T.
Kannan, R.
Mahmud, M.
Kaiser, M.S.
Belhaouari, S.B.
author_sort Punitha, S.
title Detecting COVID-19 from Lung Computed Tomography Images: A Swarm Optimised Artificial Neural Network Approach
title_short Detecting COVID-19 from Lung Computed Tomography Images: A Swarm Optimised Artificial Neural Network Approach
title_full Detecting COVID-19 from Lung Computed Tomography Images: A Swarm Optimised Artificial Neural Network Approach
title_fullStr Detecting COVID-19 from Lung Computed Tomography Images: A Swarm Optimised Artificial Neural Network Approach
title_full_unstemmed Detecting COVID-19 from Lung Computed Tomography Images: A Swarm Optimised Artificial Neural Network Approach
title_sort detecting covid-19 from lung computed tomography images: a swarm optimised artificial neural network approach
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/34329/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147307735&doi=10.1109%2fACCESS.2023.3236812&partnerID=40&md5=900b2db4aaf9a2ae1f7ca6d69d5b91f9
_version_ 1758580617424928768
score 13.211869

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