【摘要】 皮肤病是医学上的常见的、多发性疾病,因此皮肤检测技术越来越受关注。卷积神经网络是常见的皮肤检测方法,其模型结构会丢失很多信息。CapsNet(胶囊网络)是在卷积神经网络之后的一种新的神经网络。CapsNet的矢量化特征能够较好地表达空间关联性,每一个capsule(胶囊)独立地服务各自的任务。分析了CapsNet的基本结构和主要算法,改进了网络模型从而避免过拟合现象发生,试图基于改进CapsNet针对预处理之后的皮肤图像进行识别,并与传统卷积神经网络的模型作对比。实验结果表明,使用改进CapsNet对色素性皮肤病进行识别可以有较好的识别效果,并且准确率比传统方法高出8%～10%。更多还原

【Abstract】 Dermatosis is a common and multiple disease in medicine, so skin detection technology has attracted more and more at-tention. Convolutional neural network is a common skin detection method, and its model structure will lose a lot of information.CapsNet is a new kind of neural network after convolutional neural network. The vectorization of CapsNet can better express the spatial relevance, with each capsule serving its own mission independently. This paper analyzed the basic structure and main algo-rithm of CapsNet, the network model was improved to avoid over fitting, and tried to identify the pre-processed skin image based on improved CapsNet, and compared it with the model of traditional convolutional neural network. Experimental results show that improved CapsNet can be used to identify pigmented skin diseases with good effect, and the accuracy is about 8 ～ 10 percent higher than the traditional method.更多还原