Microcalcification detection using a fuzzy inference system and support vector machines

Breast cancer remains one of the deadliest diseases among women. Microcalcifications can be an early indicator of a breast cancer. Thus when they are present their detection during a screening test is very crucial. But due to their small size and low contrast in mammographies their detection is difficult. Therefore many computer aided diagnosis mathods have been developped to help and assist rediologist during their screening tests. This paper presents a novel approach to detect microcalcifications on digitized mammaographies using fuzzy logic and support vector machines. Our method was tested on 16 mammograms from Mias database including both positive and negative cases. We have obtained very satisfactory results with a sensitivity of 99,60% and a specificity of 99,11% during the learning phase.