Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 254-259.doi: 10.11896/JsJkx.190700107

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Comparative Study of DBN and CNN for Pulmonary Nodule Image Recognition

ZHANG Hua-li1, KANG Xiao-dong1, RAN Hua2, WANG Ya-ge1, LI Bo1 and BAI Fang1   

  1. 1 School of Medical Imaging,TianJin Medical University,TianJin 300203,China
    2 Qian Jiang Central Hospital of Chongqing,Chongqing 409000,China
  • Published:2020-07-07
  • About author:ZHANG Hua-li, born in 1995, M.S. candidate. Her main research interest includes medical image processing.
    RAN Hua, born in 1965, bachelor, chief physician in Chongqing QianJiang Central Hospital.His main research inte-rest includes medical imaging intervention.
  • Supported by:
    The work was supported by BeiJing-TianJin-Hebei Collaborative Innovation ProJect (17YFXTZC0020).

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Abstract: Aiming at the classification and recognition accuracy and efficiency of pulmonary nodule images,CNN model and DBN model were used to classify pulmonary nodules,and the performance of different deep learning models in pulmonary nodule image classification was evaluated.Firstly,the experiment input the pre-processed training set and label into the CNN model and the DBN model respectively to achieve the purpose of training the models.Secondly,the test set was input into the parameter-optimized model,and the accuracy,sensitivity and specificity of the classification of the two models were compared.What’s more,the classification and recognition performance of the two models was analyzed.Finally,the two models were analyzed and compared based on the three indicators:classification accuracy,sensitivity and specificity,as well as time complexity.It is found that the CNN model is more advantageous in the classification and recognition of pulmonary nodules.

Key words: CNN, DBN, Image classification and identification, Pulmonary nodules

CLC Number: 

  • TP391.41
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