A New Method to Classify Breast Cancer Tumors and Their Fractionation - Rahmani-Seryasat, Omid; Haddadnia, Javad; Ghayoumi-Zadeh, Hossein (Ciência e Natura, 2015)
In this paper, suspicious breast tumors were classified by using the neural network and the growth area method has been used for a fractionation of the benign or malignant areas of the normal tissue. Features extracted from input tissues are including statistical features and characteristics of spatial dependence. The advantage of this method is using of phase adaptive threshold based on entropy which leads to more accurate extraction of tumors and also corresponded with the nature of mammogram images. As a result, this method mimics of the human eye operation to detect abnormal masses. Database used in this paper is the MIAS mammogram database including 238 normal, benign and malignant mammograms. The accuracy obtained with 38 features is equal to 86.66% for detecting abnormal masses and 38.05 % for normal masses.