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
UR - http://www.scopus.com/inward/record.url?scp=105002710328&partnerID=8YFLogxK
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
SP - 287
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 -
