Title :
On the combination of wavelet and curvelet for feature extraction to classify lung cancer on chest radiographs
Author :
Al-Absi, Hamada R. H. ; Samir, Brahim B. ; Alhersh, Taha ; Sulaiman, Suziah
Author_Institution :
Dept. of Comput. & Inf. Sci., Univ. Teknol. PETROAS, Tronoh, Malaysia
Abstract :
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.
Keywords :
cancer; curvelet transforms; diagnostic radiography; feature extraction; image classification; image resolution; lung; medical image processing; wavelet transforms; chest radiograph; classification rate; curvelet transform; disease diagnosis; feature extraction; lung cancer classification; multiresolution method; wavelet function; wavelet level; Accuracy; Cancer; Design automation; Feature extraction; Lungs; Neural networks; Wavelet transforms;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
Conference_Location :
Osaka
DOI :
10.1109/EMBC.2013.6610340
