A breast tumor detection method based on spatial attention

Abstract

Abstract Breast cancer is the most dangerous killer for women. Accurate early diagnosis is the first step of treatment. This paper proposes a breast cancer detection model CBAMDNet based on breast pathological sections and deep learning. We adopt a pre-trained DenseNet121 embedded with spatial attention mechanism as the backbone model. Three random network models are trained in CBAMDNet to predict, and fused through majority voting to obtain more accurate results. We used a breast pathological section image data set to evaluate the generalization ability of our CBAMDNet, using 4 times cross validation. Simulation experiments show that CBAMDNet can produce higher classification results than the four existing breast cancer classification methods. Therefore, our CBAMDNet is an accurate tool to detect breast cancer and can be used for clinical diagnosis.