TY - GEN
T1 - Osteoporosis Diagnosis Based on Ultrasound Radio Frequency Signal via Multi-channel Convolutional Neural Network
AU - Chen, Zhiwei
AU - Luo, Wenqiang
AU - Zhang, Qi
AU - Lei, Baiying
AU - Wang, Tianfu
AU - Chen, Zhong
AU - Fu, Yuan
AU - Guo, Peidong
AU - Li, Changchuan
AU - Ma, Teng
AU - Ding, Yue
AU - Liu, Jiang
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Osteoporosis is a metabolic osteopathy syndrome, and the incidence of osteoporosis increases significantly with age. Currently, bone quantitative ultrasound (QUS) has been considered as a potential method for screening and diagnosing osteoporosis. However, its diagnostic accuracy is quite low. By contrast, deep learning based methods have shown the great power for extracting the most discriminative features from complex data. To improve the osteoporosis diagnostic accuracy and take advantages of QUS, we devise a deep learning method based on ultrasound radio frequency (RF) signal. Specifically, we construct a multi-channel convolutional neural network (MCNN) combined with a sliding window scheme, which can enhance the number of data as well. By using speed of sound (SOS), the quantitative experimental results of our preliminary study indicate that our proposed osteoporosis diagnosis method outperforms the conventional ultrasound methods, which may assist the clinician for osteoporosis screening.
AB - Osteoporosis is a metabolic osteopathy syndrome, and the incidence of osteoporosis increases significantly with age. Currently, bone quantitative ultrasound (QUS) has been considered as a potential method for screening and diagnosing osteoporosis. However, its diagnostic accuracy is quite low. By contrast, deep learning based methods have shown the great power for extracting the most discriminative features from complex data. To improve the osteoporosis diagnostic accuracy and take advantages of QUS, we devise a deep learning method based on ultrasound radio frequency (RF) signal. Specifically, we construct a multi-channel convolutional neural network (MCNN) combined with a sliding window scheme, which can enhance the number of data as well. By using speed of sound (SOS), the quantitative experimental results of our preliminary study indicate that our proposed osteoporosis diagnosis method outperforms the conventional ultrasound methods, which may assist the clinician for osteoporosis screening.
UR - http://www.scopus.com/inward/record.url?scp=85122504517&partnerID=8YFLogxK
U2 - 10.1109/EMBC46164.2021.9629546
DO - 10.1109/EMBC46164.2021.9629546
M3 - Conference contribution
C2 - 34891419
AN - SCOPUS:85122504517
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 832
EP - 835
BT - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Y2 - 1 November 2021 through 5 November 2021
ER -
