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Automatic COVID-19 lung infection segmentation through modified Unet model

The coronavirus (COVID-19) pandemic has had a terrible impact on human lives globally, with far-reaching consequences for the health and well-being of many people around the world. Statistically, 305.9 million people worldwide tested positive for COVID-19, and 5.48 million people died due to COVID-1...

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Main Authors: Shamim, Sania, Awan, Mazhar Javed, Mohd. Zain, Azlan, Naseem, Usman, Mohammed, Mazin Abed, Garcia-Zapirain, Begonya
Format: Article
Published: Hindawi Limited 2022
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/103251/
https://dx.doi.org/10.1155/2023/9812052
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spelling my.utm.1032512023-10-24T10:02:09Z http://eprints.utm.my/103251/ Automatic COVID-19 lung infection segmentation through modified Unet model Shamim, Sania Awan, Mazhar Javed Mohd. Zain, Azlan Naseem, Usman Mohammed, Mazin Abed Garcia-Zapirain, Begonya QA75 Electronic computers. Computer science The coronavirus (COVID-19) pandemic has had a terrible impact on human lives globally, with far-reaching consequences for the health and well-being of many people around the world. Statistically, 305.9 million people worldwide tested positive for COVID-19, and 5.48 million people died due to COVID-19 up to 10 January 2022. CT scans can be used as an alternative to time-consuming RT-PCR testing for COVID-19. This research work proposes a segmentation approach to identifying ground glass opacity or ROI in CT images developed by coronavirus, with a modified structure of the Unet model having been used to classify the region of interest at the pixel level. The problem with segmentation is that the GGO often appears indistinguishable from a healthy lung in the initial stages of COVID-19, and so, to cope with this, the increased set of weights in contracting and expanding the Unet path and an improved convolutional module is added in order to establish the connection between the encoder and decoder pipeline. This has a major capacity to segment the GGO in the case of COVID-19, with the proposed model being referred to as "convUnet."The experiment was performed on the Medseg1 dataset, and the addition of a set of weights at each layer of the model and modification in the connected module in Unet led to an improvement in overall segmentation results. The quantitative results obtained using accuracy, recall, precision, dice-coefficient, F1score, and IOU were 93.29%, 93.01%, 93.67%, 92.46%, 93.34%, 86.96%, respectively, which is better than that obtained using Unet and other state-of-the-art models. Therefore, this segmentation approach proved to be more accurate, fast, and reliable in helping doctors to diagnose COVID-19 quickly and efficiently. Hindawi Limited 2022 Article PeerReviewed Shamim, Sania and Awan, Mazhar Javed and Mohd. Zain, Azlan and Naseem, Usman and Mohammed, Mazin Abed and Garcia-Zapirain, Begonya (2022) Automatic COVID-19 lung infection segmentation through modified Unet model. Journal of Healthcare Engineering, 2022 (656698). pp. 1-14. ISSN 2040-2295 https://dx.doi.org/10.1155/2023/9812052 DOI: 10.1155/2023/9812052
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Shamim, Sania
Awan, Mazhar Javed
Mohd. Zain, Azlan
Naseem, Usman
Mohammed, Mazin Abed
Garcia-Zapirain, Begonya
Automatic COVID-19 lung infection segmentation through modified Unet model
description The coronavirus (COVID-19) pandemic has had a terrible impact on human lives globally, with far-reaching consequences for the health and well-being of many people around the world. Statistically, 305.9 million people worldwide tested positive for COVID-19, and 5.48 million people died due to COVID-19 up to 10 January 2022. CT scans can be used as an alternative to time-consuming RT-PCR testing for COVID-19. This research work proposes a segmentation approach to identifying ground glass opacity or ROI in CT images developed by coronavirus, with a modified structure of the Unet model having been used to classify the region of interest at the pixel level. The problem with segmentation is that the GGO often appears indistinguishable from a healthy lung in the initial stages of COVID-19, and so, to cope with this, the increased set of weights in contracting and expanding the Unet path and an improved convolutional module is added in order to establish the connection between the encoder and decoder pipeline. This has a major capacity to segment the GGO in the case of COVID-19, with the proposed model being referred to as "convUnet."The experiment was performed on the Medseg1 dataset, and the addition of a set of weights at each layer of the model and modification in the connected module in Unet led to an improvement in overall segmentation results. The quantitative results obtained using accuracy, recall, precision, dice-coefficient, F1score, and IOU were 93.29%, 93.01%, 93.67%, 92.46%, 93.34%, 86.96%, respectively, which is better than that obtained using Unet and other state-of-the-art models. Therefore, this segmentation approach proved to be more accurate, fast, and reliable in helping doctors to diagnose COVID-19 quickly and efficiently.
format Article
author Shamim, Sania
Awan, Mazhar Javed
Mohd. Zain, Azlan
Naseem, Usman
Mohammed, Mazin Abed
Garcia-Zapirain, Begonya
author_facet Shamim, Sania
Awan, Mazhar Javed
Mohd. Zain, Azlan
Naseem, Usman
Mohammed, Mazin Abed
Garcia-Zapirain, Begonya
author_sort Shamim, Sania
title Automatic COVID-19 lung infection segmentation through modified Unet model
title_short Automatic COVID-19 lung infection segmentation through modified Unet model
title_full Automatic COVID-19 lung infection segmentation through modified Unet model
title_fullStr Automatic COVID-19 lung infection segmentation through modified Unet model
title_full_unstemmed Automatic COVID-19 lung infection segmentation through modified Unet model
title_sort automatic covid-19 lung infection segmentation through modified unet model
publisher Hindawi Limited
publishDate 2022
url http://eprints.utm.my/103251/
https://dx.doi.org/10.1155/2023/9812052
_version_ 1781777668603117568
score 13.211869

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