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.
@article{arxiv.1904.07834,
title = {Double Transfer Learning for Breast Cancer Histopathologic Image Classification},
author = {Jonathan de Matos and Alceu de S. Britto and Luiz E. S. Oliveira and Alessandro L. Koerich},
journal= {arXiv preprint arXiv:1904.07834},
year = {2019}
}