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Journal > International Journal of Electrical and Computer Engineering (IJECE) > A New Approach Based on Quantum Clustering and Wavelet Transform for Breast Cancer Classification: Comparative Study

 

International Journal of Electrical and Computer Engineering (IJECE)
Vol 5, No 5: October 2015
A New Approach Based on Quantum Clustering and Wavelet Transform for Breast Cancer Classification: Comparative Study
Hamdi, Nezha ( Cadi Ayyad University)
Auhmani, Khalid ( Cadi Ayyad University)
Hassani, Moha.M’Rabet ( Cadi Ayyad University)
Article Info   ABSTRACT
Published date:
01 Oct 2015
 
Feature selection involves identifying a subset of the most useful features that produce the same results as the original set of features. In this paper, we present a new approach for improving classification accuracy. This approach is based on quantum clustering for feature subset selection and wavelet transform for features extraction. The feature selection is performed in three steps. First the mammographic image undergoes a wavelet transform then some features are extracted. In the second step the original feature space is partitioned in clusters in order to group similar features. This operation is performed using the Quantum Clustering algorithm. The third step deals with the selection of a representative feature for each cluster. This selection is based on similarity measures such as the correlation coefficient (CC) and the mutual information (MI). The feature which maximizes this information (CC or MI) is chosen by the algorithm. This approach is applied for breast cancer classification. The K-nearest neighbors (KNN) classifier is used to achieve the classification. We have presented classification accuracy versus feature type, wavelet transform and K neighbors in the KNN classifier. An accuracy of 100% was reached in some cases.
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