Abstract：Breast cancer is one of the most common cancers among women worldwide. Early detection, diagnosis and treatment are necessary to elevate survival rates and quality of life for breast cancer patients. Mammography has been recognized as the most popular modality for breast cancer screening. However, it is quite challenging to interpret mammograms accurately primarily due to the overlapping of tissues, under the universal screening model which yields a large number of mammographic images. Many methods have been developed for computer-aided diagnosis schemes to classify patients into benign and malignant with mammography. These models are useful for early breast cancer detection, while approaches are needed to improve accuracy and reduce the time and costs. In this paper, we proposed a new lightweight deep convolutional neural network to classify breast cancer rapidly with mammograms. A public digital database for screening mammography, the Digital Database for Screening Mammography (DDSM), was used in this study. The dataset contains 2 604 patients, of which 854 cases were pathologically confirmed benign, 141 cases were benign without callback, 914 patients were malignant confirmed by biopsy and pathology examinations and 695 were negative patients. We first enhanced the data with image processing methods including rotating, flipping, brightening, adding salt-and-pepper noise and Gaussian noise. The post-processing image dataset has 48 300 mammograms, including 5 208 original mammographic images and 43 092 enhanced images. We proposed a deep neural network model named classification of mammograms convolutional neural network (CMCNNet) by embedding VGGNet and inception modules to classify breast cancer with mammograms. Results showed that the new network architecture achieved an accuracy of 99.38% at a learning rate of 0.000 5. In addition, we figured out that when the number of convolution kernels was set to 32-32-32-32 corresponding to the convolutional layer group, and the size of the convolutional kernel was set to 3×3, the accuracy increased to 99.60%. Finally, the proposed model was validated on 48 300 mammographic images (2 604 patients). A three-fold cross-validation method was applied to different models using AlexNet, VGGNet, Inception-V3, ResNet, as well as the CMCNNet. Results showed that CMCNNet achieved an accuracy of 99.86% that outperformed all the other models adopted in this study with the same iteration numbers for training. In addition, the CMCNNet model needed the least training time. Results of the parameter optimization study and the new network model proposed in this paper have profound practical significance for the classification of breast cancer using mammography.
蔡绿君, 傅奕萱, 阮耀平, 李颜娥. 基于快速深度卷积神经网络的乳腺钼靶影像分类方法[J]. 中国生物医学工程学报, 2026, 45(4): 469-481.
Cai Lvjun, Fu Yixuan, Ruan Yaoping, Li Yane. A Fast Deep Convolutional Neural Network for Mammograms Classification. Chinese Journal of Biomedical Engineering, 2026, 45(4): 469-481.
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