Brain Hemorrhage Segmentation in CT Scan Images using Deep Learning based Approach

Haibin Zhang; Worapan Kusakunniran; Thanongchai Siriapisith; Pairash Saiviroonporn

doi:10.1109/TENCON55691.2022.9977491

Brain Hemorrhage Segmentation in CT Scan Images using Deep Learning based Approach

Mahidol University

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

Abstract

In this paper, a variety of neural networks are compared, and the optimal CE-Net model is found and improved. It can segment CT images of cerebral hemorrhage, especially for the small and irregular images.

Original language	English
Title of host publication	Proceedings of 2022 IEEE Region 10 International Conference, TENCON 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665450959
DOIs	https://doi.org/10.1109/TENCON55691.2022.9977491
Publication status	Published - 2022
Event	2022 IEEE Region 10 International Conference, TENCON 2022 - Virtual, Online, Hong Kong Duration: 1 Nov 2022 → 4 Nov 2022

Publication series

Name	IEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume	2022-November
ISSN (Print)	2159-3442
ISSN (Electronic)	2159-3450

Conference

Conference	2022 IEEE Region 10 International Conference, TENCON 2022
Country/Territory	Hong Kong
City	Virtual, Online
Period	1/11/22 → 4/11/22

Keywords

CE-Net
CT Scan Images
Deep Learning
Neural Networks
Segmentation
U-Net

Access to Document

10.1109/TENCON55691.2022.9977491

Cite this

Zhang, H., Kusakunniran, W., Siriapisith, T., & Saiviroonporn, P. (2022). Brain Hemorrhage Segmentation in CT Scan Images using Deep Learning based Approach. In Proceedings of 2022 IEEE Region 10 International Conference, TENCON 2022 (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2022-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENCON55691.2022.9977491

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title = "Brain Hemorrhage Segmentation in CT Scan Images using Deep Learning based Approach",

abstract = "In this paper, a variety of neural networks are compared, and the optimal CE-Net model is found and improved. It can segment CT images of cerebral hemorrhage, especially for the small and irregular images.",

keywords = "CE-Net, CT Scan Images, Deep Learning, Neural Networks, Segmentation, U-Net",

author = "Haibin Zhang and Worapan Kusakunniran and Thanongchai Siriapisith and Pairash Saiviroonporn",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Region 10 International Conference, TENCON 2022 ; Conference date: 01-11-2022 Through 04-11-2022",

year = "2022",

doi = "10.1109/TENCON55691.2022.9977491",

language = "English",

series = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings of 2022 IEEE Region 10 International Conference, TENCON 2022",

}

Zhang, H, Kusakunniran, W , Siriapisith, T & Saiviroonporn, P 2022, Brain Hemorrhage Segmentation in CT Scan Images using Deep Learning based Approach. in Proceedings of 2022 IEEE Region 10 International Conference, TENCON 2022. IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 2022-November, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE Region 10 International Conference, TENCON 2022, Virtual, Online, Hong Kong, 1/11/22. https://doi.org/10.1109/TENCON55691.2022.9977491

Brain Hemorrhage Segmentation in CT Scan Images using Deep Learning based Approach. / Zhang, Haibin; Kusakunniran, Worapan ; Siriapisith, Thanongchai et al.
Proceedings of 2022 IEEE Region 10 International Conference, TENCON 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2022-November).

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

TY - GEN

T1 - Brain Hemorrhage Segmentation in CT Scan Images using Deep Learning based Approach

AU - Zhang, Haibin

AU - Kusakunniran, Worapan

AU - Siriapisith, Thanongchai

AU - Saiviroonporn, Pairash

PY - 2022

Y1 - 2022

N2 - In this paper, a variety of neural networks are compared, and the optimal CE-Net model is found and improved. It can segment CT images of cerebral hemorrhage, especially for the small and irregular images.

AB - In this paper, a variety of neural networks are compared, and the optimal CE-Net model is found and improved. It can segment CT images of cerebral hemorrhage, especially for the small and irregular images.

KW - CE-Net

KW - CT Scan Images

KW - Deep Learning

KW - Neural Networks

KW - Segmentation

KW - U-Net

UR - https://www.scopus.com/pages/publications/85145657571

U2 - 10.1109/TENCON55691.2022.9977491

DO - 10.1109/TENCON55691.2022.9977491

M3 - Conference contribution

AN - SCOPUS:85145657571

T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON

BT - Proceedings of 2022 IEEE Region 10 International Conference, TENCON 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE Region 10 International Conference, TENCON 2022

Y2 - 1 November 2022 through 4 November 2022

ER -

Zhang H, Kusakunniran W , Siriapisith T , Saiviroonporn P. Brain Hemorrhage Segmentation in CT Scan Images using Deep Learning based Approach. In Proceedings of 2022 IEEE Region 10 International Conference, TENCON 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (IEEE Region 10 Annual International Conference, Proceedings/TENCON). doi: 10.1109/TENCON55691.2022.9977491

Brain Hemorrhage Segmentation in CT Scan Images using Deep Learning based Approach

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Keywords

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