计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 254-259.doi: 10.11896/JsJkx.190700107

• 计算机图形学 & 多媒体 • 上一篇    下一篇

用于肺结节影像分类识别的DBN与CNN的比较研究

张华丽1, 康晓东1, 冉华2, 王亚鸽1, 李博1, 白放1   

  1. 1 天津医科大学医学影像学院 天津 300203;
    2 重庆市黔江中心医院 重庆 409000
  • 发布日期:2020-07-07
  • 通讯作者: 冉华(1319466165@qq.com)
  • 作者简介:aszhanghuali@163.com
  • 基金资助:
    京津冀协同创新项目(17YFXTZC0020)

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).

摘要: 针对肺结节图像的分类识别精度和效率问题,分别将CNN(Convolution Neural Network)模型和DBN(Deep Belief Network)模型用于肺结节分类识别,并评估不同的深度学习模型在肺结节图像分类方面的性能。首先,实验将预处理过的训练集和标签分别输入到CNN模型和DBN模型,达到训练模型的目的;其次,将测试集输入到参数最优的模型中,比较两种模型测试集分类的准确率、敏感性和特异性,并分析两种模型的分类识别性能。最后,从分类准确率、敏感性和特异性3个指标以及时间复杂度来分析比较两种模型,发现CNN模型在肺结节图像分类识别上更有优越性。

关键词: CNN, DBN, 肺结节, 图像分类识别

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

中图分类号: 

  • TP391.41
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