[1]徐力平),尚丹),陈小玉)#.模糊神经网络在肺癌CT诊断中的应用*[J].郑州大学学报(医学版),2014,(02):191.
 XU Liping,SHANG Dan,CHEN Xiaoyu.Application of fuzzy neural network to the diagnosis of lung cancer by CT[J].JOURNAL OF ZHENGZHOU UNIVERSITY(MEDICAL SCIENCES),2014,(02):191.
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模糊神经网络在肺癌CT诊断中的应用*
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《郑州大学学报(医学版)》[ISSN:1671-6825/CN:41-1340/R]

卷:
期数:
2014年02期
页码:
191
栏目:
论著
出版日期:
2014-03-20

文章信息/Info

Title:
Application of fuzzy neural network to the diagnosis of lung cancer by CT
作者:
徐力平1)尚丹1)陈小玉2)#
1)郑州大学信息工程学院 郑州 450001 2)郑州大学公共卫生学院卫生毒理学教研室 郑州 450001
Author(s):
XU Liping1)SHANG Dan1) CHEN Xiaoyu2)
1)School of Information Engineering, Zhengzhou University, Zhengzhou 450001 2)Department of Health Toxicology,College of Public Health, Zhengzhou University, Zhengzhou 450001
关键词:
模糊神经网络隶属度函数肺癌诊断
Keywords:
fuzzy neural networkmembership functionlung cancerdiagnosis
分类号:
A
文献标志码:
TP18;R734.2
摘要:
目的:综合运用模糊数学和人工神经网络知识构建一个模糊神经网络(FNN)模型,用于肺癌计算机辅助诊断。方法:以实际肺癌诊断病例(n=117)中的一部分(n=73)作为训练集,首先利用隶属度函数对样本的5个临床参数和21项CT特征进行模糊化处理,再输入基于BP算法的神经网络,对网络进行训练。用训练好的网络对余下的样本(n=44)进行预测,并将预测结果以及基于BP神经网络(BPNN)的预测结果与病理结果进行比较。结果:FNN诊断肺癌的灵敏度、特异度和正确率分别为0.904 8、0.913 0和90.91%,BPNN分别为0.809 5、0.869 6和84.09%。结论:FNN模型诊断肺癌的预测结果与病理结果接近,且优于BPNN的预测结果。
Abstract:
Aim: To develop a computeraided scheme of the lung cancer diagnosis by CT based on fuzzy neural networks(FNN) to assist radiologists in distinguishing malignant tumor from benign pulmonary nodules. Methods: With a part of actual lung cancer diagnosis cases as samples(n=73), first, sample data were treated with membership function. Then, the treated data were used as input of the neural networks based on backpropagation algorithm and the neural networks was trained. The other cases(n=44) were used as validation data and were forecasted by the trained FNN. The result of FNN and that of the usual backpropagation neural network (BPNN) were both compared with the pathological results. Results: The sensitivity, specificity and accuracy of the FNN in the diagnosis of lung cancer were 0.904 8, 0.913 0 and 90.91%, and those for BPNN were 0.809 5, 0.869 6 and 84.09%. Conclusion: The forecasting results by FNN is more consistent with that of pathology than that by the BPNN model.

参考文献/References:

1]杨玲,李连弟,陈育德,等.中国肺癌死亡趋势分析及发病、死亡的估计与预测[J].中国肺癌杂志,2005,8(4):274 [2]Matsuki Y,Nakamura K,Watanabe H,et al.Usefulness of an artificial neural network for differentiating benign from malignant pulmonary nodules on highresolution CT: evaluation with receiver operating characteristic analysis[J].Am J Roentgenol,2002,178(3):657 [3]Coppini G,Diciotti S,Falchini M,et al.Neural networks for computeraided diagnosis: detection of lung nodules in chest radiograms[J].IEEE Trans Inf Technol Biomed,2003,7(4):344 [4]Nakamura K,Yoshida H,Engelmann R,et al.Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks[J].Radiology,2000,214(3):823 [5]蒋中明,徐卫亚,张新敏.弹性介质模糊有限元控制方程的快速解法[J].工程力学,2006,23(7):25

备注/Memo

备注/Memo:
*河南省教育厅科学技术研究重点项目12A510024 #通讯作者,女,1957年11月生,教授,研究方向:职业与健康,Email:chenxiaoyu@zzu.edu.cn
更新日期/Last Update: 2014-04-23