Cancer is one of the leading causes of death in many countries. Breast cancer is one of the most common cancers in women. Especially in remote areas with low medical standards, the diagnosis efficiency of breast cancer is extremely low due to insufficient medical facilities and doctors. Therefore, in-depth research on how to improve the diagnosis rate of breast cancer has become a hot spot. With the development of society and science, people use artificial intelligence to improve the auxiliary diagnosis of diseases in the existing medical system, which can become a solution for detecting and accurately diagnosing breast cancer. The paper proposes an auxiliary diagnosis model that uses deep learning in view of the low rate of human diagnosis by doctors in remote areas. The model uses classic convolutional neural networks, including VGG16, InceptionV3, and ResNet50 to extract breast cancer image features, then merge these features, and finally train the model VIRNets for auxiliary diagnosis. Experimental results prove that for the recognition of benign and malignant breast cancer pathological images under different magnifications, VIRNets have a high generalization and strong robustness, and their accuracy is better than their basic network and other structures of the network. Therefore, the solution provides a certain practical value for assisting doctors in the diagnosis of breast cancer in real scenes.

中文翻译：

---

基于卷积神经网络的特征融合用于乳腺癌辅助诊断

在许多国家，癌症是导致死亡的主要原因之一。乳腺癌是女性最常见的癌症之一。尤其是在医疗水平不高的偏远地区，由于医疗设施和医生不足，乳腺癌的诊断效率极低。因此，深入研究如何提高乳腺癌的诊断率成为研究热点。随着社会和科学的发展，人们利用人工智能来完善现有医疗体系中疾病的辅助诊断，可以成为检测和准确诊断乳腺癌的解决方案。针对偏远地区医生人工诊断率低的问题，论文提出了一种使用深度学习的辅助诊断模型。该模型使用经典的卷积神经网络，包括 VGG16、InceptionV3和ResNet50提取乳腺癌图像特征，然后合并这些特征，最后训练模型VIRNets进行辅助诊断。实验结果证明，对于不同放大倍数下的良恶性乳腺癌病理图像的识别，VIRNets具有较高的泛化性和较强的鲁棒性，其准确性优于其基础网络和其他结构的网络。因此，该方案为辅助医生在真实场景中诊断乳腺癌提供了一定的实用价值。实验结果证明，对于不同放大倍数下的良恶性乳腺癌病理图像的识别，VIRNets具有较高的泛化性和较强的鲁棒性，其准确性优于其基础网络和其他结构的网络。因此，该方案为辅助医生在真实场景中诊断乳腺癌提供了一定的实用价值。实验结果证明，对于不同放大倍数下的良恶性乳腺癌病理图像的识别，VIRNets具有较高的泛化性和较强的鲁棒性，其准确性优于其基础网络和其他结构的网络。因此，该方案为辅助医生在真实场景中诊断乳腺癌提供了一定的实用价值。