Computer Science ›› 2019, Vol. 46 ›› Issue (5): 247-253.doi: 10.11896/j.issn.1002-137X.2019.05.038

Special Issue: Medical Imaging

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Melanoma Classification Method by Integrating Deep Convolutional Residual Network

HU Hai-gen1, KONG Xiang-yong1, ZHOU Qian-wei1, GUAN Qiu1, CHEN Sheng-yong1,2   

  1. (College of Computer Science & Technology,Zhejiang University of Technology,Hangzhou 310024,China)1
    (School of Computer Science and Engineering,Tianjin University of Technology,Tianjin 300384,China)2
  • Published:2019-05-15

Abstract: To solve the classification problems of melanoma,such as low contrast,indistinguishable by the naked eyes,mass information interference,small dataset and data imbalance,this paper proposed an integrated classification method based on mask data augment and deep convolutional residual network.Firstly,according to the characteristics of skin lesion image and the previous researches,two data augmentation methods by masking the partial area of the trainingima-ges were proposed.Secondly,on the basis of these two data augmentation methods,some features were extracted by using deep convolutional residual 50-layer network.Thirdly,two different classification models were constructed and integrated based on these features.Finally,a series of experiments were conducted based on the datasets of Internal Skin Imaging Collaboration (ISIC) 2016 Challenge competition.The experimental results show that the integrated classification structure model can overcome the deficiencies of a single convolution residual network in melanoma classification tasks,and can achieve better classification results than other methods on skin lesion dataset with less training examples,and multiple evaluation indicators in the proposed method are better than the top-5 results in the ISIC2016 Challenge competition.

Key words: Convolution residual network, Data augmentation, Ensemble learning, Melanoma

CLC Number: 

  • TP183
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