  This work proposes a classification approach for breast cancer
histopathologic images (HI) that uses transfer learning to extract features
from HI using an Inception-v3 CNN pre-trained with ImageNet dataset. We also
use transfer learning on training a support vector machine (SVM) classifier on
a tissue labeled colorectal cancer dataset aiming to filter the patches from a
breast cancer HI and remove the irrelevant ones. We show that removing
irrelevant patches before training a second SVM classifier, improves the
accuracy for classifying malign and benign tumors on breast cancer images. We
are able to improve the classification accuracy in 3.7% using the feature
extraction transfer learning and an additional 0.7% using the irrelevant patch
elimination. The proposed approach outperforms the state-of-the-art in three
out of the four magnification factors of the breast cancer dataset.
