  Breast cancer is the most common invasive cancer in women, and the second
main cause of death. Breast cancer screening is an efficient method to detect
indeterminate breast lesions early. The common approaches of screening for
women are tomosynthesis and mammography images. However, the traditional manual
diagnosis requires an intense workload by pathologists, who are prone to
diagnostic errors. Thus, the aim of this study is to build a deep convolutional
neural network method for automatic detection, segmentation, and classification
of breast lesions in mammography images. Based on deep learning the Mask-CNN
(RoIAlign) method was developed to features selection and extraction; and the
classification was carried out by DenseNet architecture. Finally, the precision
and accuracy of the model is evaluated by cross validation matrix and AUC
curve. To summarize, the findings of this study may provide a helpful to
improve the diagnosis and efficiency in the automatic tumor localization
through the medical image classification.
