TY - GEN
T1 - Lung Segmentation using MultiResUNet CNN based on Computed Tomography Image
AU - Ferdinandus, Fx
AU - Setiawan, Esther Irawati
AU - Yuniarno, Eko Mulyanto
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Lung segmentation is the first step in medical image processing to determine various lung diseases. Currently, the image segmentation process will be more optimal by using deep learning through the convolution process. Various Convolution Neural Network (CNN) based architectures for image segmentation were created by many researchers, however U-Net is the current state of the art for medical image segmentation. Nevertheless, the modification of U-Net continues, and MultiResUNet is one of the new architectures claimed to be better. In this study, we use MultiResUNet for lung segmentation on Computed Tomography (CT) images as the first step to Covid-19 infection segmentation, and the results will be compared using the U-Net architecture. Based on the results of the segmentation experiment, we got satisfactory results. Using the Mean-IoU evaluation metric, it was concluded that the MultiResUNet score was slightly better than the U-Net score for patient lung segmentation, where there was an increase in the score of 1.33% (MultiResUNet=93.05%, U-Net=91.83%) in the dataset which we use.
AB - Lung segmentation is the first step in medical image processing to determine various lung diseases. Currently, the image segmentation process will be more optimal by using deep learning through the convolution process. Various Convolution Neural Network (CNN) based architectures for image segmentation were created by many researchers, however U-Net is the current state of the art for medical image segmentation. Nevertheless, the modification of U-Net continues, and MultiResUNet is one of the new architectures claimed to be better. In this study, we use MultiResUNet for lung segmentation on Computed Tomography (CT) images as the first step to Covid-19 infection segmentation, and the results will be compared using the U-Net architecture. Based on the results of the segmentation experiment, we got satisfactory results. Using the Mean-IoU evaluation metric, it was concluded that the MultiResUNet score was slightly better than the U-Net score for patient lung segmentation, where there was an increase in the score of 1.33% (MultiResUNet=93.05%, U-Net=91.83%) in the dataset which we use.
KW - CNN
KW - Deep Learning
KW - Lung segmentation
KW - MultiResUNet
KW - Semantic Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85137901589&partnerID=8YFLogxK
U2 - 10.1109/ISITIA56226.2022.9855353
DO - 10.1109/ISITIA56226.2022.9855353
M3 - Conference contribution
AN - SCOPUS:85137901589
T3 - 2022 International Seminar on Intelligent Technology and Its Applications: Advanced Innovations of Electrical Systems for Humanity, ISITIA 2022 - Proceeding
SP - 1
EP - 6
BT - 2022 International Seminar on Intelligent Technology and Its Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022
Y2 - 20 July 2022 through 21 July 2022
ER -