Evaluating the impact of polygonal representations on mass classification

Breast cancer is one of the most severe types of cancer and responsible for about 15% of all women death related to cancer worldwide. Mammography is the most reliable and recommended approach to help early detection of breast cancer, since this technique can detect the disease in its asymptomatic phase. Different Computer-Aided Diagnosis (CAD) systems have been used to aid in the diagnosis serving a second opinion to the physicians. These systems extract features from masses found in mammograms, which compose inputs to the classifiers used in CAD systems. The main contribution of this study is the evaluation of different polygonal representations on the phase of feature extraction. In this context, two different polygonal models were tested with different parameters to represent boundaries of masses. Artificial neural networks, support vector machines and k-nearest neighbors were used to discriminate masses as benign and malignant achieving accuracy of 85%, 84% and 85%, respectively.