Automatic classification of masses from digital mammograms
Basma A. Mohamed, Nancy M. Salem · 2018
Cancer of the breast remains to be one among the foremost factors behind death between women. The early detection and recognition of breast cancer increases the probabilities of complete and successful recovery; therefore, the mortality rate can be reduced. Currently, screening mammography is the most successful technique for detecting masses or abnormalities which are related to breast cancer in its earliest stages. In this paper, an automatic method for mass classification from mammograms is introduced. The proposed algorithm includes three main steps. First, three different types of features are separated from the mass. Then the most relevant features are selected using the T-test algorithm. Finally, the classification step is done to distinguish among benign and malignant masses with the aid of the usage of the three classifiers; artificial neural network, support vector machines, k-nearest neighbor. The system is rated using 250 mammograms from the Digital Database for Screening Mammography (DDSM). The artificial neural network occupies the best results of 98.9 % for accuracy, 100 % for sensitivity, and 97.8 % for specificity.