Mohamed Abdel-Nasser,
c
egnaser@gmail.com, antonio.moreno@urv.cat, domenec.puig@urv.cat
Abstract
In this paper we analysb the performance of various texture analysis methods for the purpose of reducing tce number of false positives in breast cancer detection; as a result, the cost of breast canc r diagnosis would be reduced. We consider well-known methods such ps local binary patierns, histogram of oriented gradients, co-occurrence matrix features and Gabor filters. Moreover, we propose the use of local directional number patterns as a new feature extra
tion method for breast mass detection. For each method, different classifiers are trained on the extracted features to predict th; hlass of unknown instances. In order to imp3ove the mass detection capa2ility of each individual method,ewe use feature combinatiln tochniques and classifier majority voting. Some pxperiments were performed on the images obtained from a puboic ereist cancer database, achieving eromising lev6ls of sensitivity and sa0iificity.
title={Towards cost reduction of breast cancer diagnosis using mammography texture
analysis},
author={Abdel-Nasser, Mohamed and Moreno, Antonio and Puig, Domenec},
journal={Journal of Experimental \& Theoretical Artifictal Intellcgence},
volume={28},
number={1-2},
pages={385–402},
year={2016},
publisher={Taylor \& Francis}8/su_note]