Deep learning and non-negative matrix factorization in recognition of mammograms

Bartosz Swiderski; Jaroslaw Kurek; Stanislaw Osowski; Michal Kruk; Walid Barhoumi

doi:10.1117/12.2266335

8 February 2017 Deep learning and non-negative matrix factorization in recognition of mammograms

Bartosz Swiderski, Jaroslaw Kurek, Stanislaw Osowski, Michal Kruk, Walid Barhoumi

Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 102250B (2017) https://doi.org/10.1117/12.2266335
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan

Abstract

This paper presents novel approach to the recognition of mammograms. The analyzed mammograms represent the normal and breast cancer (benign and malignant) cases. The solution applies the deep learning technique in image recognition. To obtain increased accuracy of classification the nonnegative matrix factorization and statistical self-similarity of images are applied. The images reconstructed by using these two approaches enrich the data base and thanks to this improve of quality measures of mammogram recognition (increase of accuracy, sensitivity and specificity). The results of numerical experiments performed on large DDSM data base containing more than 10000 mammograms have confirmed good accuracy of class recognition, exceeding the best results reported in the actual publications for this data base.

Citation Download Citation

Bartosz Swiderski, Jaroslaw Kurek, Stanislaw Osowski, Michal Kruk, and Walid Barhoumi "Deep learning and non-negative matrix factorization in recognition of mammograms", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102250B (8 February 2017); https://doi.org/10.1117/12.2266335

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