Novel methods of image description and ensemble of classifiers in application to mammogram analysis

Abstract The paper proposes new advanced methods of image description and an ensemble of classifiers for recognition of mammograms in breast cancer. The non-negative matrix factorization and many other advanced methods of image representation, not exploited in the field of mammogram recognition, are developed and checked in the role of diagnostic features. Final image recognition is done by using an ensemble of classifiers. The new approach to the integration of an ensemble is proposed. It applies the weighted majority voting with the weights determined from the optimization task defined on the basis of the area under curve of ROC. The results of numerical experiments performed on large data base “Digital Database for Screening Mammography” containing more than 10,000 mammograms have confirmed superior accuracy in recognition of abnormal from the normal cases. The presented results of class recognition exceed the best achievements for this base reported in the actual publications.

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