# Double Transfer Learning for Breast Cancer Histopathologic Image Classification Source: [Double Transfer Learning for Breast Cancer Histopathologic Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:30:43.473Z Authors: Jonathan de Matos, Alceu de S. Britto Jr., Luiz E. S. Oliveira, Alessandro L. Koerich Published: 2019-04-16T17:26:34.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()