Quantum Clustering-Based Feature Subset Selection for Mammographic Image Classification

In this paper, we present an algorithm for feature selection. This algorithm labeled QC-FS: Quantum Clustering for Feature Selection performs the selection in two steps. Partitioning the original features space in order to group similar features is performed using the Quantum Clustering algorithm. Then the selection of a representative for each cluster is carried out. It uses similarity measures such as correlation coefficient (CC) and the mutual information (MI). The feature which maximizes this information is chosen by the algorithm.

This study is carried out for mammographic image classification. It is performed in three stages: extraction of features characterizing the tissue areas then a feature selection was achieved by the proposed algorithm and finally the classification phase was carried out. We have used the KNN classifier to perform the classification task. We have presented classification accuracy versus feature type. Results show that Zernike moments allowed an accuracy of 99.5% with preprocessed images.