Chinese Journal of Intelligent Science and Technology ›› 2021, Vol. 3 ›› Issue (4): 474-481.doi: 10.11959/j.issn.2096-6652.202147

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Classification method of dermoscopic image based on hierarchical convolution neural network

Hong SHAO, Mingkun ZHANG, Wencheng CUI   

  1. School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China
  • Revised:2021-02-24 Online:2021-12-15 Published:2021-12-01


In order to solve the problem of insufficient number of dermoscopic image and the imbalance of image data among various diseases, a classification method of dermoscopic image based on class weighted cross entropy loss function and hierarchical convolution neural network was proposed.Firstly, the dermoscopic image was processed by color constancy to eliminate the ambient light noise.Then, the hierarchical convolution neural network based on ResNet 50 was constructed, and the two classification and multi classification convolution neural network models were constructed respectively, and the class weighted cross entropy loss function was set according to the quantitative characteristics of the dermatoscopic image.The experimental results show that the method achieves good classification effect, and the classification accuracy reaches 85.94%.Compared with the improved classification model ResNet 50, the test accuracy is improved by 5.752%.

Key words: ResNet 50, classification of dermoscopic image, hierarchical convolution neural network, over fitting

CLC Number: 

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