Comparative Analysis of Machine Learning Methods for Mass Classification in Mammograms - Melo, Matheus Cordeiro de; Gajadhar, Andy Anand; Batista, Leonardo Vidal (Proceedings of the Brazilian Symposium on Computing Applied to Health (SBCAS), 2014)
The breast cancer is a common cancer among women and mammography is important for early detection of the disease. This work presents a comparative analysis of methods for classification, as malignant or benign, of masses found in mammographic images. The methodology consists in building a test database, mass segmentation, attributes extraction and the classification through machine learning algorithms, using the Weka tool. Tests show that the best CCR was 85.09% achieved with Multilayer Perceptron algorithm.