TY - GEN
T1 - Carotid Artery Segmentation on Ultrasound Image using Deep Learning based on Non-Local Means-based Speckle Filtering
AU - Pramulen, Aji Sapta
AU - Yuniarno, Eko Mulyanto
AU - Nugroho, J.
AU - Sunarya, I. Made Gede
AU - Purnama, I. Ketut Eddy
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/17
Y1 - 2020/11/17
N2 - Cardiovascular disease (CVD) causes significant deaths worldwide, of which 17.3 million deaths per year are due to CVD. The use of Ultrasound is necessary to see the abnormalities. The study will segment Carotid Artery segmentation on the Ultrasound image by using the U-Net-based architecture of non-local means-based speckle filtering (NLMBSF). The images will use NLMBSF to reduce speckles, and the data set will be divided into two parts, namely the dataset, which using NLMBSF and not NLMBSF. After that, doing training to create a U-net model, the training data model results will be searched with the best Accuracy. The obtained result of the study is an accuracy value of 97.74%, dice value is 87.22%, and a loss of 0.0107 on data that does not use NLMBSF. Still, it got different data results using NLMBSF, namely 97.6% accuracy, dice value is 84.06% and 0.0138 value loss.
AB - Cardiovascular disease (CVD) causes significant deaths worldwide, of which 17.3 million deaths per year are due to CVD. The use of Ultrasound is necessary to see the abnormalities. The study will segment Carotid Artery segmentation on the Ultrasound image by using the U-Net-based architecture of non-local means-based speckle filtering (NLMBSF). The images will use NLMBSF to reduce speckles, and the data set will be divided into two parts, namely the dataset, which using NLMBSF and not NLMBSF. After that, doing training to create a U-net model, the training data model results will be searched with the best Accuracy. The obtained result of the study is an accuracy value of 97.74%, dice value is 87.22%, and a loss of 0.0107 on data that does not use NLMBSF. Still, it got different data results using NLMBSF, namely 97.6% accuracy, dice value is 84.06% and 0.0138 value loss.
KW - Carotid Artery
KW - Segmentation
KW - U-Net
KW - Ultrasound
UR - http://www.scopus.com/inward/record.url?scp=85099639522&partnerID=8YFLogxK
U2 - 10.1109/CENIM51130.2020.9298009
DO - 10.1109/CENIM51130.2020.9298009
M3 - Conference contribution
AN - SCOPUS:85099639522
T3 - CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020
SP - 360
EP - 365
BT - CENIM 2020 - Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020
Y2 - 17 November 2020 through 18 November 2020
ER -
