TY - GEN
T1 - On the combination of wavelet and curvelet for feature extraction to classify lung cancer on chest radiographs
AU - Al-Absi, Hamada R.H.
AU - Samir, Brahim Belhaouari
AU - Alhersh, Taha
AU - Sulaiman, Suziah
PY - 2013
Y1 - 2013
N2 - This paper investigates the combination of multiresolution methods for feature extraction for lung cancer. The focus is on the impact of combining wavelet and curvelet on the accuracy of the disease diagnosis. The paper investigates feature extraction with two different levels of wavelet, two different wavelet functions and the combination of wavelet and curvelet to obtain a high classification rate. The findings suggest the potential of combining different multiresolution methods in achieving high accuracy rates.
AB - This paper investigates the combination of multiresolution methods for feature extraction for lung cancer. The focus is on the impact of combining wavelet and curvelet on the accuracy of the disease diagnosis. The paper investigates feature extraction with two different levels of wavelet, two different wavelet functions and the combination of wavelet and curvelet to obtain a high classification rate. The findings suggest the potential of combining different multiresolution methods in achieving high accuracy rates.
UR - http://www.scopus.com/inward/record.url?scp=84886449882&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2013.6610340
DO - 10.1109/EMBC.2013.6610340
M3 - Conference contribution
C2 - 24110527
AN - SCOPUS:84886449882
SN - 9781457702167
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3674
EP - 3677
BT - 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
T2 - 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Y2 - 3 July 2013 through 7 July 2013
ER -