Breast Cancer: Classification of Tumors Using Machine Learning Algorithms

Breast cancer is currently one of the leading causes of death among women worldwide. Masses are considered significant signs of the existence of malignant lesions, as they occur in most breast cancer cases. However, their detection is challenging since masses have large variation in shape, margin, size and arc often indistinguishable from surrounding tissue, making the radiologist's task tedious in the case where a significant number of mammograms require fast and accurate interpretation. For these reasons, computer-aided diagnosis (CAD) systems are being developed to make the diagnostic process easier for radiologists. In these systems, segmentation and classification of breast masses in mammograms are important steps. This work aims to evaluate the performance of machine learning techniques in classifying tumors into benign and malignant. The selected techniques were applied on 1663 mammograms from the Digital Database for Screening Mammography. Of the 1663 images, 769 images correspond to malignant cases, and 894 correspond to benign cases. The efficiency of each of the considered techniques was evaluated by using four metrics, namely, the false positive rate, sensitivity, specificity, and accuracy.