Breast cancer classification method based on improved VGG16 using mammography images
Zhaoqi Liu, Jidong Peng, Xiumei Guo, Shaoqiong Chen, Liansheng Liu
Abstract
Breast cancer has become the leading global cancer, and early detection and diagnosis of breast cancer are of paramount importance for treatment. This paper proposes a breast X-ray mammography image classification model based on Convolutional Neural Network (CNN). The model categorizes breast X-ray mammography images into benign and malignant classes. Built upon the VGG network, the model adjusts the network structure and conducts experiments on the dataset collected and organized by the Medical Imaging Department of Ganzhou People's Hospital and The Sixth Affiliated Hospital of Jinan University. To address the issue of imbalanced data in the dataset, the model employs a focal loss function for optimization and combines transfer learning and data augmentation strategies during network training. Experimental results demonstrate that the model achieves an average recognition rate of 96.945% across four different magnification levels. Notably, recognition rates exceed 95.5% for the 50X, 100X, and 200× magnification levels, demonstrating excellent classification capabilities. This model ignificantly improving classification accuracy compared to previous models, which provides meaningful insights into the classification of breast X-ray mammography images.