On the combination of wavelet and curvelet for feature extraction to classify lung cancer on chest radiographs

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 differ...

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Bibliographic Details
Main Authors: Al-Absi, Hamada R. H., Samir, Brahim Belhaouari, Alhersh, Taha, Sulaiman, Suziah
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Cancer
Design automation
Feature extraction
Lungs
Neural networks
Wavelet transforms
Online Access:Request full text
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Summary: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.
ISSN:1094-687X
1558-4615
2694-0604
DOI:10.1109/EMBC.2013.6610340