Fuzzy Neural Networks and Genetic Algorithms for Medical Images Interpretation

Nacéra Benamrane, A. Aribi, L. Kraoula
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引用次数: 31

Abstract

In this paper, we propose an approach for detection and specification of anomalies present in medical images. The idea is to combine three metaphors: neural networks, fuzzy logic and genetic algorithms in a hybrid system. The neural networks and fuzzy logic metaphors are coupled in one system called fuzzy neural networks. The genetic algorithm adds to this hybridizing the property of total research like an initialization of the fuzzy neural networks training algorithm witch is based on an adapted version of the back propagation algorithm. After applying the growing region algorithm to extract regions, the fuzzy neural network detect the suspect regions, which are interpreted by the fuzzy neural network of specification. Some of experimental results on brain images show the feasibility of the proposed approach
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模糊神经网络和遗传算法在医学图像解释中的应用
在本文中,我们提出了一种检测和规范医学图像中存在的异常的方法。这个想法是在一个混合系统中结合三个隐喻:神经网络、模糊逻辑和遗传算法。神经网络和模糊逻辑隐喻耦合在一个称为模糊神经网络的系统中。遗传算法增加了这种杂交的总体研究性质,就像模糊神经网络训练算法的初始化一样,它是基于一个改编版本的反向传播算法。采用生长区域算法提取区域后,模糊神经网络检测可疑区域,并用规范模糊神经网络对可疑区域进行解释。一些脑图像的实验结果表明了该方法的可行性
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