COVID-19 Lung CT Image Segmentation: A Comparison of Various U-Net Variants

David Olayemi Alebiosu; Adeola Folayan; Wei Chen; Abejide Tolulope; Samuel Soma M. Ajibade

doi:10.1007/978-981-96-3949-6_22

COVID-19 Lung CT Image Segmentation: A Comparison of Various U-Net Variants

David Olayemi Alebiosu^*
, Adeola Folayan
, Wei Chen
, Abejide Tolulope
, Samuel Soma M. Ajibade

^*Corresponding author for this work

School of Intelligent Manufacturing Ecosystem

Sunway University
Monash University Malaysia
University of Derby
Istanbul Ticaret University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

Lung segmentation has become a bedrock in the effective diagnosis, and classification of coronavirus (COVID-19) from radiological images such as computed tomography (CT) and X-ray images. Since the coronavirus (COVID-19) discovery, several methods have been employed to segment the COVID-19-infected areas from lung CT images. One of the most popular segmentation methods is the U-Net model. U-Net is a convolutional neural network used for medical image segmentation. U-Net and its variants have become a more reliable architecture used for medical image segmentation. U-Net models have produced outstanding results in segmenting diseases such as COVID-19 from lung CT images. The exceptional results produced by the U-Net model have inspired various researchers to explore the potential of U-Net for various segmentation tasks. This study compares the performances of recently used state-of-the-art U-Net models on lung CT images for tuberculosis segmentation.

Original language	English
Title of host publication	Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics
Editors	Wei Chen, Andrew Huey Ping Tan, Yang Luo, Long Huang, Yuyi Zhu, Anwar PP Abdul Majeed, Fan Zhang, Yuyao Yan, Chenguang Liu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	287-294
Number of pages	8
ISBN (Print)	9789819639489
DOIs	https://doi.org/10.1007/978-981-96-3949-6_22
Publication status	Published - 2025
Event	2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Suzhou, China Duration: 22 Aug 2024 → 23 Aug 2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1316 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024
Country/Territory	China
City	Suzhou
Period	22/08/24 → 23/08/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

COVID-19
Lung CT Image
Segmentation
U-Net

Access to Document

10.1007/978-981-96-3949-6_22

Cite this

Alebiosu, D. O., Folayan, A., Chen, W., Tolulope, A., & Ajibade, S. S. M. (2025). COVID-19 Lung CT Image Segmentation: A Comparison of Various U-Net Variants. In W. Chen, A. H. Ping Tan, Y. Luo, L. Huang, Y. Zhu, A. PP Abdul Majeed, F. Zhang, Y. Yan, & C. Liu (Eds.), Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics (pp. 287-294). (Lecture Notes in Networks and Systems; Vol. 1316 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-3949-6_22

Alebiosu, David Olayemi ; Folayan, Adeola ; Chen, Wei et al. / COVID-19 Lung CT Image Segmentation : A Comparison of Various U-Net Variants. Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics. editor / Wei Chen ; Andrew Huey Ping Tan ; Yang Luo ; Long Huang ; Yuyi Zhu ; Anwar PP Abdul Majeed ; Fan Zhang ; Yuyao Yan ; Chenguang Liu. Springer Science and Business Media Deutschland GmbH, 2025. pp. 287-294 (Lecture Notes in Networks and Systems).

@inproceedings{9aeec326bca7494ab2b4de1036f9bdcf,

title = "COVID-19 Lung CT Image Segmentation: A Comparison of Various U-Net Variants",

abstract = "Lung segmentation has become a bedrock in the effective diagnosis, and classification of coronavirus (COVID-19) from radiological images such as computed tomography (CT) and X-ray images. Since the coronavirus (COVID-19) discovery, several methods have been employed to segment the COVID-19-infected areas from lung CT images. One of the most popular segmentation methods is the U-Net model. U-Net is a convolutional neural network used for medical image segmentation. U-Net and its variants have become a more reliable architecture used for medical image segmentation. U-Net models have produced outstanding results in segmenting diseases such as COVID-19 from lung CT images. The exceptional results produced by the U-Net model have inspired various researchers to explore the potential of U-Net for various segmentation tasks. This study compares the performances of recently used state-of-the-art U-Net models on lung CT images for tuberculosis segmentation.",

keywords = "COVID-19, Lung CT Image, Segmentation, U-Net",

author = "Alebiosu, \{David Olayemi\} and Adeola Folayan and Wei Chen and Abejide Tolulope and Ajibade, \{Samuel Soma M.\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 ; Conference date: 22-08-2024 Through 23-08-2024",

year = "2025",

doi = "10.1007/978-981-96-3949-6\_22",

language = "English",

isbn = "9789819639489",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "287--294",

editor = "Wei Chen and \{Ping Tan\}, \{Andrew Huey\} and Yang Luo and Long Huang and Yuyi Zhu and \{PP Abdul Majeed\}, Anwar and Fan Zhang and Yuyao Yan and Chenguang Liu",

booktitle = "Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics",

}

Alebiosu, DO, Folayan, A, Chen, W, Tolulope, A & Ajibade, SSM 2025, COVID-19 Lung CT Image Segmentation: A Comparison of Various U-Net Variants. in W Chen, AH Ping Tan, Y Luo, L Huang, Y Zhu, A PP Abdul Majeed, F Zhang, Y Yan & C Liu (eds), Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics. Lecture Notes in Networks and Systems, vol. 1316 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 287-294, 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024, Suzhou, China, 22/08/24. https://doi.org/10.1007/978-981-96-3949-6_22

COVID-19 Lung CT Image Segmentation: A Comparison of Various U-Net Variants. / Alebiosu, David Olayemi; Folayan, Adeola; Chen, Wei et al.
Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics. ed. / Wei Chen; Andrew Huey Ping Tan; Yang Luo; Long Huang; Yuyi Zhu; Anwar PP Abdul Majeed; Fan Zhang; Yuyao Yan; Chenguang Liu. Springer Science and Business Media Deutschland GmbH, 2025. p. 287-294 (Lecture Notes in Networks and Systems; Vol. 1316 LNNS).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - COVID-19 Lung CT Image Segmentation

T2 - 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

AU - Alebiosu, David Olayemi

AU - Folayan, Adeola

AU - Chen, Wei

AU - Tolulope, Abejide

AU - Ajibade, Samuel Soma M.

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

PY - 2025

Y1 - 2025

N2 - Lung segmentation has become a bedrock in the effective diagnosis, and classification of coronavirus (COVID-19) from radiological images such as computed tomography (CT) and X-ray images. Since the coronavirus (COVID-19) discovery, several methods have been employed to segment the COVID-19-infected areas from lung CT images. One of the most popular segmentation methods is the U-Net model. U-Net is a convolutional neural network used for medical image segmentation. U-Net and its variants have become a more reliable architecture used for medical image segmentation. U-Net models have produced outstanding results in segmenting diseases such as COVID-19 from lung CT images. The exceptional results produced by the U-Net model have inspired various researchers to explore the potential of U-Net for various segmentation tasks. This study compares the performances of recently used state-of-the-art U-Net models on lung CT images for tuberculosis segmentation.

AB - Lung segmentation has become a bedrock in the effective diagnosis, and classification of coronavirus (COVID-19) from radiological images such as computed tomography (CT) and X-ray images. Since the coronavirus (COVID-19) discovery, several methods have been employed to segment the COVID-19-infected areas from lung CT images. One of the most popular segmentation methods is the U-Net model. U-Net is a convolutional neural network used for medical image segmentation. U-Net and its variants have become a more reliable architecture used for medical image segmentation. U-Net models have produced outstanding results in segmenting diseases such as COVID-19 from lung CT images. The exceptional results produced by the U-Net model have inspired various researchers to explore the potential of U-Net for various segmentation tasks. This study compares the performances of recently used state-of-the-art U-Net models on lung CT images for tuberculosis segmentation.

KW - COVID-19

KW - Lung CT Image

KW - Segmentation

KW - U-Net

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U2 - 10.1007/978-981-96-3949-6_22

DO - 10.1007/978-981-96-3949-6_22

M3 - Conference Proceeding

AN - SCOPUS:105002710328

SN - 9789819639489

T3 - Lecture Notes in Networks and Systems

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EP - 294

BT - Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics

A2 - Chen, Wei

A2 - Ping Tan, Andrew Huey

A2 - Luo, Yang

A2 - Huang, Long

A2 - Zhu, Yuyi

A2 - PP Abdul Majeed, Anwar

A2 - Zhang, Fan

A2 - Yan, Yuyao

A2 - Liu, Chenguang

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 22 August 2024 through 23 August 2024

ER -

Alebiosu DO, Folayan A, Chen W, Tolulope A, Ajibade SSM. COVID-19 Lung CT Image Segmentation: A Comparison of Various U-Net Variants. In Chen W, Ping Tan AH, Luo Y, Huang L, Zhu Y, PP Abdul Majeed A, Zhang F, Yan Y, Liu C, editors, Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics. Springer Science and Business Media Deutschland GmbH. 2025. p. 287-294. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-96-3949-6_22

COVID-19 Lung CT Image Segmentation: A Comparison of Various U-Net Variants

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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