Deep learning and non-negative matrix factorization in recognition of mammograms
Bartosz Świderski, Jarosław Kurek, S. Osowski, Michał Kruk, Walid Barhoumi · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2017
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.