胶囊网络皮肤癌对图像特征的性质和空间关系进行编码，克服卷积神经网络池化过程信息丢失的缺点。针对胶囊网络中只能提取浅层特征和压缩函数收敛性能问题，提出了ResNeXt与胶囊网络级联的Rs-Capsnet网络。首先采用ResNeXt网络中学习图像的复杂特征，利用Inception模块和残差连接提取深层特征；通过CBAM注意力模块调整特征图权重并将其输送到胶囊模块；然后使用改进的压缩函数的胶囊网络完成分类，最后将改进后的网络同主流模型进行对比。结果表明：Rs-Capsnet在皮肤癌图像分类上表现了更佳的性能。

Capsule networks can encode the properties and spatial relationships of skin cancer image features, thereby overcoming the disadvantage of information loss in the pooling process of convolutional neural networks. Aiming at the problem that only shallow features can be extracted and the convergence performance of the squash function in capsule networks, a ResNeXt cascaded with capsule networks was proposed for Rs-Capsnet networks. Firstly, the complex features of the image were learned using the ResNeXt network. The Inception module and the residual connection were used to extract the deep features, and the weights of the feature map were adjusted and delivered to the capsule module through the CBAM attention module. Then, an improved squash function capsule network was used to complete the classification. Finally, the improved network was compared with mainstream models. The results show that Rs-Capsnet exhibits better performance in skin cancer image classification.