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.

中文翻译：

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基于改进的VGG16的乳腺X线图像乳腺癌分类方法

乳腺癌已成为全球第一大癌症，乳腺癌的早期发现和诊断对于治疗至关重要。本文提出一种基于卷积神经网络（CNN）的乳腺X射线乳腺X线摄影图像分类模型。该模型将乳房 X 射线乳房 X 光检查图像分为良性和恶性两类。该模型以VGG网络为基础，调整网络结构，并在赣州市人民医院医学影像科和暨南大学附属第六医院收集整理的数据集上进行实验。为了解决数据集中数据不平衡的问题，该模型采用焦点损失函数进行优化，并在网络训练过程中结合迁移学习和数据增强策略。实验结果表明，该模型在四种不同放大倍数下的平均识别率达到了96.945%。值得注意的是，在 50 倍、100 倍和 200 倍放大级别下，识别率超过 95.5%，展示了出色的分类能力。与之前的模型相比，该模型显着提高了分类准确性，为乳腺 X 射线乳房 X 线摄影图像的分类提供了有意义的见解。