中圖分類號(hào)： TN98,；TP391.4 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.200121 中文引用格式： 李勵(lì)澤,，張晨潔，楊曉慧,，等. 基于改進(jìn)CapsNet的色素性皮膚病識(shí)別的研究[J].電子技術(shù)應(yīng)用,，2020，46(11)：60-64. 英文引用格式： Li Lize,，Zhang Chenjie,，Yang Xiaohui，et al. Pigmented skin lesion recognition based on improved CapsNet[J]. Application of Electronic Technique,，2020,，46(11)：60-64.
Pigmented skin lesion recognition based on improved CapsNet
Li Lize，Zhang Chenjie,，Yang Xiaohui,，Sun Wenbin，Guo Bin
School of Electronics and Information Engineering, Changchun University of Science and Technology,，Changchun 130022,，China
Abstract： Dermatosis is a common and multiple disease in medicine, so skin detection technology has attracted more and more attention. 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 algorithm 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.
Key words : pigmented skin lesion；skin image recognition,；neural network,；CapsNet
