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一个医学图像分类器的设计
引用本文:李丙春,耿国华,周明全,孙蕾.一个医学图像分类器的设计[J].计算机工程与应用,2004,40(17):230-232.
作者姓名:李丙春  耿国华  周明全  孙蕾
作者单位:西北大学计算机系,西安,710069
基金项目:国家自然科学基金项目(编号:60271032)资助
摘    要:提出了一个基于径向基函数网络的医学图像分类器。该系统包括图像预处理、特征提取、分类器的构造几个部分。在网络构造上,文章采用了自适应的网络结构调整技术,提高了网络的泛化能力,同时,在网络权值调节上采用了具有全局优化的模拟退火算法,避免了陷入局部极小的缺陷。实验结果表明,该模型系统达到了76.6%的准确率,辅助系统可以极大地提高医学图像分类的效率和准确性。

关 键 词:径向基函数网络  图像分类  模拟退火算法  医学图像  乳腺X线照片
文章编号:1002-8331-(2004)17-0230-03

The Design of a Medical Images Classifier
Li,Bingchun Geng Guohua Zhou Mingquan Sun Lei.The Design of a Medical Images Classifier[J].Computer Engineering and Applications,2004,40(17):230-232.
Authors:Li  Bingchun Geng Guohua Zhou Mingquan Sun Lei
Abstract:This paper proposes a medical images classifier based on radial basis function neural network.The system we propose consists of a pre-processing phase,a feature extraction phase and a building the classifier phase.The system uses adaptive network structure adjustment techniques to improve the generalization capability of the network.At the same time ,to keep the network from getting into local minimum the system uses simulated annealing with feature of global optimize to adjust the network weight.The experimental results show that the system performs well reaching about 76.6%in accuracy.With the help of the system physicians can improve efficiency and accuracy in the medical image classification.
Keywords:radial basis function networks  images classification  simulated annealing  medical images  mammography
本文献已被 CNKI 维普 万方数据 等数据库收录!
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