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This work proposes a classification approach for breast cancerhistopathologic images (HI) that uses transfer learning to extract featuresfrom HI using an Inception-v3 CNN pre-trained with ImageNet dataset. We alsouse transfer learning on training a support vector machine (SVM) classifier ona tissue labeled colorectal cancer dataset aiming to filter the patches from abreast cancer HI and remove the irrelevant ones. We show that removingirrelevant patches before training a second SVM classifier, improves theaccuracy for classifying malign and benign tumors on breast cancer images. Weare able to improve the classification accuracy in 3.7% using the featureextraction transfer learning and an additional 0.7% using the irrelevant patchelimination. The proposed approach outperforms the state-of-the-art in threeout of the four magnification factors of the breast cancer dataset.

Double Transfer Learning for Breast Cancer Histopathologic Image Classification