A Comparative Study of Dimension Reduction Methods Combined with Wavelet Transform Applied to the Classification of Mammographic Images

In this paper, we present a comparative study of dimension reduction methods combined with wavelet transform. This study is carried out for mammographic image classification. It is performed in three stages: extraction of features characterizing the tissue areas then a dimension reduction was achieved by four different methods of discrimination and finally the classification phase was carried. We have late compared the performance of two classifiers KNN and decision tree.

Results show the classification accuracy in some cases has reached 100%. We also found that generally the classification accuracy increases with the dimension but stabilizes after a certain value which is approximately d=60.