Computer Science ›› 2020, Vol. 47 ›› Issue (9): 129-134.doi: 10.11896/jsjkx.190700203

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Multi-branch Convolutional Neural Network for Lung Nodule Classification and Its Interpretability

ZHANG Jia-jia, ZHANG Xiao-hong   

  1. School of Big Data & Software Engineering,Chongqing University,Chongqing 400000,China
  • Received:2019-07-29 Published:2020-09-10
  • About author:ZHANG Jia-jia,born in 1994,postgra-duate.Her main research interests include medical image analysis,deep learning and so on.
    ZHANG Xiao-hong,born in 1973,professor.His main research interests include data mining of software enginee-ring,topic modeling,image semantic analysis and video analysis.
  • Supported by:
    National Natural Science Foundation of China (61772093) and Chongqing Major Theme Projects (cstc2018jszx-cyztzxX0017).

Abstract: The characteristics of lung nodules are complex and diverse,which make it difficult to classify lung nodules.Although more and more deep learning models are applied to the lung nodule classification task of computer-aided lung cancer diagnosis systems,the “black box” characteristics of these models cannot explain what knowledge the model has learned from the data and how the knowledge influences the decision,leading to a lack of reliability in the diagnosis results.To this end,an interpretable multi-branch convolutional neural network model is proposed to identify the benign and malignant lung nodules.The model uses the semantic features of the pulmonary nodules which radiologists use in diagnosis to assist identifying the benign and malignant lung nodules.These characteristics are combined with the branch of malignancy classification into a multi-branch network.Then beyond the malignancy classification,the model can predict nodule attributes,which could potentially explain the diagnosis result.Experimental results on the LIDC-IDRI dataset show that,compared with the existing methods,the proposed model can not only obtain interpretable diagnostic results,but also achieve better classification of lung nodules with an accuracy rate of 97.8%.

Key words: Computer-aided diagnosis, Convolutional neural network, Multi-branch, Interpretable, Classification of malignant degree of pulmonary nodules

CLC Number: 

  • TP183
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