偏头痛作为一种常见的疾病, 发病概率高, 致病机理尚不明确, 并且临床缺乏有效的诊断手段. 运用功能核磁共振成像技术获取被试脑功能数据, 然后通过深度学习中自动编码器, 自动提取数据特征, 并结合各种机器学习算法, 预测偏头痛, 为临床诊断提供参考依据. 用深度学习提取数据特征, 训练分类器, 能达到更好的分类效果. 深度学习算法可以在传统模板获取初步特征之后, 进一步提取更加精细有效的特征, 在预测偏头痛上获得更好的分类性能.%The migraine is a common disease with high incidence. It is still not easy to explain its pathogenesis very well. Therefore, it lacks effective diagnostic methods. This study aims to predict migraine by using the functional magnetic resonance imaging technology to obtain functional network of brain, then through deep learning of automatically it extracts data features by Autoencoder, combined with various machine learning algorithms to provide a reference for clinical diagnosis of physicians. It can get better classification effect to extract data features and train the classifier by deep learning. The deep learning algorithm, based on the initial features obtained by the traditional templates, can further extract more fine and effective features, and obtain better classification performance in predicting migraine.
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