高等学校化学学报 ›› 1994, Vol. 15 ›› Issue (7): 982.

• 论文 • 上一篇    下一篇

老年性白内障晶状体中金属元素的人工神经网分析

蔡淑安1, 许禄2, 杨翌秋2, 胡昌玉2, P. C. Jors3, J. W. Ball3, S. L. Dixon3   

  1. 1. 白求恩医科大学一院;
    2. 中国科学院长春应用化学研究所, 长春, 130022;
    3. 宾夕法尼亚州立大学化学系, 美国
  • 收稿日期:1993-08-21 修回日期:1994-04-22 出版日期:1994-07-24 发布日期:1994-07-24
  • 通讯作者: 许禄.
  • 作者简介:蔡淑安,女,52岁,副教授.

Classification of Human Senile Cataract Lenses Based on Metal Contents Using Neural Networks

CAI Shu-An1, XU Lu2, YANG Yi-Qiu2, HU Chang-Yu2, P. C. Jors3, J. W. Ball3, S. L. Dixon3   

  1. 1. The First Clinical College, Norman Bethune University of Medical Sciences;
    2. Changchun Institute of Applied Chemistry, Academia Sinica, Changchun, 130022;
    3. Department of Chemistry, The Pennsylvania State University, USA
  • Received:1993-08-21 Revised:1994-04-22 Online:1994-07-24 Published:1994-07-24

摘要: 由K、Na、Ca、Mg、Fe、Cu、Zn和Mn在老年性晶状体中的含量,运用人工神经网法成功地将老年性白内障、白内障晶状体核和正常人晶状体划分为3类。同时讨论了神经网的结构(层数及每层的结点数)、初始权重等对神经网性能的影响。随机地将30个晶状体分为训练集和测试集,其识别率及预测率均达到100%.

关键词: 神经网, 老年性白内障, 痕量金属元素

Abstract: Senile cataract lenses.nuclei from cataract lenses, and normal lenses were successfully separated into three classes using quasi-Newton neural networks. Tlie lenses were classified based on the concentrations of K, Na, Ca, Mg, Cu, Fe, Zn and Mn measured by atomic absorption spectroscopy.The 30 cataract lenses used in this study were randomly divided into a 23 member training set and 7 member test set.The architecture including the number of layers, the number of neurons in each layer, and the initial weights were varied in order to study their effects on the performance of the neural network.Once trained, the neural network correctly classified all 30 cataract lenses.

Key words: Neural network, Human senile cataract, Trace metal elements

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