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Wavelet-Packet Approach Combined with Linear Discriminant Analysis for Breast Cancer Classification

K. Auhmani, N. Hamdi, M. M. Hassani


This paper presents a study on classification of digital mammographic images using an approach combining wavelet packet transform analysis and linear discriminant analysis. A comparative evaluation between different wavelet analysis architectures in terms of their classification ability is carried out. For 107 regions of interest (ROI), 56 normal and 51 abnormal, texture features were extracted using co-occurrence-based method of Haralick. A set of features obtained from wavelet packet analysis was evaluated. Method of dimensionality reduction was employed to overcome the curse of dimensionality while preserving a good classification rate. The classification results using k-nearest-neighbors (KNN) classifiers show that the proposed methodology can be used to classify digitized mammograms. The best obtained accuracy is 97,62% corresponding to the third level wavelet packet decomposition.


Breast cancer, linear discriminant analysis, pattern recognition, Wavelet Packet Decomposition

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