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

fYear

2013

fDate

3-7 July 2013

Firstpage

3674

Lastpage

3677

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE

Conference_Location

Osaka

ISSN

1557-170X

Type

conf

DOI

10.1109/EMBC.2013.6610340

Filename

6610340