Breast cancer is one of the most common and dangerous cancers in women, while it can also afflict men. Breast cancer treatment and detection are greatly aided by the use of histopathological images since they contain sufficient phenotypic data. A Deep Neural Network (DNN) is commonly employed to improve accuracy and breast cancer detection. In our research, we have analyzed pre-trained deep transfer learning models such as ResNet50, ResNet101, VGG16, and VGG19 for detecting breast cancer using the 2453 histopathology images dataset. Images in the dataset were separated into two categories: those with invasive ductal carcinoma (IDC) and those without IDC. After analyzing the transfer learning model, we found that ResNet50 outperformed other models, achieving accuracy rates of 90.2%, Area under Curve (AUC) rates of 90.0%, recall rates of 94.7%, and a marginal loss of 3.5%.

本研究使用2453个组织病理学图像数据集，分离有侵袭性导管癌（IDC）和无IDC的数据，分析了预训练的深度迁移学习模型如ResNet50、ResNet101、VGG16和VGG19在检测乳腺癌方面的应用，研究发现ResNet50模型的准确率可达90.2％，曲线下面积（AUC）率为90.0％，召回率为94.7％，不适当损失为3.5％。

基于迁移学习的乳腺癌组织病理图像检测深度分析