TY  - CONF
EP  - 3677
CY  - Osaka
A1  - Al-Absi, H.R.H.
A1  - Samir, B.B.
A1  - Alhersh, T.
A1  - Sulaiman, S.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886449882&doi=10.1109%2fEMBC.2013.6610340&partnerID=40&md5=b11d4462bc1b0e02566d6857df320720
N1  - cited By 3; Conference of 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 ; Conference Date: 3 July 2013 Through 7 July 2013; Conference Code:100170
ID  - scholars3389
Y1  - 2013///
TI  - On the combination of wavelet and curvelet for feature extraction to classify lung cancer on chest radiographs
KW  - Accuracy rate; Chest radiographs; Classification rates; Curvelets; Different wavelets; Disease diagnosis; Lung Cancer; Multiresolution methods
KW  -  Biological organs; Diseases; Feature extraction
KW  -  Diagnosis
KW  -  algorithm; computer assisted diagnosis; human; image processing; lung tumor; procedures; radiography; sensitivity and specificity; thorax radiography; wavelet analysis
KW  -  Algorithms; Diagnosis
KW  -  Computer-Assisted; Humans; Image Processing
KW  -  Computer-Assisted; Lung Neoplasms; Radiography
KW  -  Thoracic; Sensitivity and Specificity; Wavelet Analysis
SN  - 1557170X
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. © 2013 IEEE.
AV  - none
SP  - 3674
ER  -