中圖分類號： TN98；TP391.4
文獻標識碼： A
DOI：10.16157/j.issn.0258-7998.200121
中文引用格式： 李勵澤，張晨潔，楊曉慧，等. 基于改進CapsNet的色素性皮膚病識別的研究[J].電子技術應用，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

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