A New Syntactic Approach for Masses Classification in Digital Mammograms

Abstract

Breast cancer is one of the most common cancers that affect women worldwide being responsible for about 15% of all deaths related to cancer in the world. Mammography is one of the main techniques to help early detection of breast cancer. Although there are some characteristics that should be considered to discriminate benign and malignant masses, only about 15 to 30% of the cases sent to biopsies are malignant. To aid in the diagnosis of this disease, several CAD systems were proposed and developed to make a second opinion to the physicians, but the theory of formal languages is underexplored in this field. This paper presents a new syntactic approach to discriminate benign and malignant masses in digital mammography. Preliminary results showed that this approach is very promising, since our classifier achieved accuracies from 80% to 100% depending on the model and features used, applied on two different databases.