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%.

翻译：乳腺癌是女性中最常见且最危险的癌症之一，但也可能影响男性。由于组织病理图像包含足够的表型数据，其在乳腺癌治疗和检测中具有重要辅助作用。深度神经网络（DNN）常被用于提高乳腺癌检测的准确率。本研究利用2453张组织病理图像数据集，分析了ResNet50、ResNet101、VGG16和VGG19等预训练深度迁移学习模型在乳腺癌检测中的性能。数据集中的图像被分为两类：含浸润性导管癌（IDC）的样本与不含IDC的样本。通过分析迁移学习模型，我们发现ResNet50优于其他模型，其准确率达到90.2%，曲线下面积（AUC）为90.0%，召回率为94.7%，边际损失为3.5%。