  Breast cancer is one of the deadliest cancer worldwide. Timely detection
could reduce mortality rates. In the clinical routine, classifying benign and
malignant tumors from ultrasound (US) imaging is a crucial but challenging
task. An automated method, which can deal with the variability of data is
therefore needed.
  In this paper, we compared different Convolutional Neural Networks (CNNs) and
transfer learning methods for the task of automated breast tumor
classification. The architectures investigated in this study were VGG-16 and
Inception V3. Two different training strategies were investigated: the first
one was using pretrained models as feature extractors and the second one was to
fine-tune the pre-trained models. A total of 947 images were used, 587
corresponded to US images of benign tumors and 360 with malignant tumors. 678
images were used for the training and validation process, while 269 images were
used for testing the models.
  Accuracy and Area Under the receiver operating characteristic Curve (AUC)
were used as performance metrics. The best performance was obtained by fine
tuning VGG-16, with an accuracy of 0.919 and an AUC of 0.934. The obtained
results open the opportunity to further investigation with a view of improving
cancer detection.
