A fusion deep learning framework based on breast cancer grade prediction

In breast cancer grading, the subtle differences between HE-stained pathological images and the insufficient number of data samples lead to grading inefficiency. With its rapid development, deep learning technology has been widely used for automatic breast cancer grading based on pathological images. In this paper, we propose an integrated breast cancer grading framework based on a fusion deep learning model, which uses three different convolutional neural networks as submodels to extract feature information at different levels from pathological images. Then, the output features of each submodel are learned by the fusion network based on stacking to generate the final decision results. To validate the effectiveness and reliability of our proposed model, we perform dichotomous and multiclassification experiments on the Invasive Ductal Carcinoma (IDC) pathological image dataset and a generated dataset and compare its performance with those of the state-of-the-art models. The classification accuracy of the proposed fusion network is 93.8%, the recall is 93.5%, and the F1 score is 93.8%, which outperforms the state-of-the-art methods.