A New Method Based-Gentle Adaboost and Wavelet Transform for Breast Cancer Classification

2019 ◽  
pp. 51-63
Author(s):  
Nezha Hamdi ◽  
Khalid Auhmani ◽  
Moha Mrabet Hassani ◽  
Omar Elkharki
Keyword(s):  
Breast Cancer ◽  
Wavelet Transform ◽  
Cancer Classification ◽  
New Method ◽  
Breast Cancer Classification

scholarly journals A New Approach Based on Quantum Clustering and Wavelet Transform for Breast Cancer Classification: Comparative Study

2015 ◽  
Vol 5 (5) ◽  
pp. 1027
Author(s):  
Nezha Hamdi ◽  
Khalid Auhmani ◽  
Moha.M’Rabet Hassani
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Wavelet Transform ◽  
Classification Accuracy ◽  
Cancer Classification ◽  
Feature Subset Selection ◽  
New Approach ◽  
Breast Cancer Classification ◽  
Knn Classifier ◽  
Quantum Clustering

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.

scholarly journals A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability

2009 ◽  
Vol 10 (1) ◽  
Author(s):  
Herman MJ Sontrop ◽  
Perry D Moerland ◽  
René van den Ham ◽  
Marcel JT Reinders ◽  
Wim FJ Verhaegh
Keyword(s):  
Breast Cancer ◽  
Sensitivity Analysis ◽  
Cancer Classification ◽  
Breast Cancer Classification ◽  
Feature Variability

scholarly journals Haralick texture and invariant moments features for breast cancer classification

2016 ◽  
Author(s):  
Siti Salmah Yasiran ◽  
Shaharuddin Salleh ◽  
Rozi Mahmud
Keyword(s):  
Breast Cancer ◽  
Cancer Classification ◽  
Breast Cancer Classification ◽  
Invariant Moments
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