首页> 外文期刊>Journal of Neuroscience Methods >A cellular neural network based method for classification of magnetic resonance images: Towards an automated detection of hippocampal sclerosis.
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A cellular neural network based method for classification of magnetic resonance images: Towards an automated detection of hippocampal sclerosis.

机译:基于细胞神经网络的磁共振图像分类方法:旨在自动检测海马硬化。

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We present a cellular neuronal network (CNN) based approach to classify magnetic resonance images with and without hippocampal or Ammon's horn sclerosis (AHS) in the medial temporal lobe. A CNN combines the architecture of cellular automata and artificial neural networks and is an array of locally coupled nonlinear electrical circuits or cells, which is capable of processing a large amount of information in parallel and in real time. Using an exemplary database that consists of a large number of volumes of interest extracted from T1-weighted magnetic resonance images from 144 subjects we here demonstrate that the network allows to classify brain tissue with respect to the presence or absence of mesial temporal sclerosis. Results indicate the general feasibility of CNN-based computer-aided systems for diagnosis and classification of images generated by medical imaging systems.
机译:我们提出了一种基于细胞神经元网络(CNN)的方法来对内侧颞叶中有无海马或阿蒙角硬化症(AHS)的磁共振图像进行分类。 CNN结合了细胞自动机和人工神经网络的架构,是一系列局部耦合的非线性电路或单元,能够并行和实时处理大量信息。我们使用一个示例数据库,该数据库由从144位受试者的T1加权磁共振图像中提取的大量感兴趣的数据组成,我们在此证明该网络可以根据脑膜颞叶硬化的存在或不存在对脑组织进行分类。结果表明,基于CNN的计算机辅助系统对于医学成像系统生成的图像的诊断和分类具有普遍的可行性。

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