Benign and malignant classification of mammogram images based on deep learning

Abstract Breast cancer is one of the most common malignant tumors in women, which seriously affect women's physical and mental health and even threat to life. At present, mammography is an important criterion for doctors to diagnose breast cancer. However, due to the complex structure of mammogram images, it is relatively difficult for doctors to identify breast cancer features. At present, deep learning is the most mainstream image classification algorithm. Therefore, this study proposes an improved DenseNet neural network model, also known as the DenseNet-II neural network model, for the effective and accurate classification of benign and malignant mammography images. Firstly, the mammogram images are preprocessed. Image normalization avoids interference from light, while the adoption of data enhancement prevents over-fitting cause by small data set. Secondly, the DenseNet neural network model is improved, and a new DenseNet-II neural network model is invented, which is to replace the first convolutional layer of the DenseNet neural network model with the Inception structure. Finally, the pre-processed mammogram datasets are input into AlexNet, VGGNet, GoogLeNet, DenseNet network model and DenseNet-II neural network model, and the experimental results are analyzed and compared. According to the 10-fold cross validation method, the results show that the DenseNet-II neural network model has better classification performance than other network models. The average accuracy of the model reaches 94.55%, which improves the accuracy of the benign and malignant classification of mammogram images. At the same time, it also proves that the model has good generalization and robustness.