认知障碍脑功能磁共振图像的孪生网络特征工程算法

Siamese Network Based Feature Engineering Algorithmfor Encephalopathy fMRI Images

功能磁共振成像技术(fMRI:functional Magnetic Resonance Imaging)是一种高效的脑成像技术研究方法,为减少fMRI数据的冗余,将其转换为更具分类潜力的特征,提出一个基于孪生网络(SANet:Siamese Network)的特征构造算法SANet,将多个扫描点下的脑区信息类比为图,应用改进的AlexNet网络进行特征构造,并结合增量特征选择策略达到优化分类的目的。通过实验对比3种不同网络结构和4种分类器对SANet模型的影响,并进行消融实验,验证增量特征选择算法对SANet构造特征的分类效果。实验表明,SANet模型能对fMRI数据进行有效构造,且提高原始特征的分类性能。

fMRI(functional Magnetic Resonance imaging)is an efficient research method for brain imaging technique.In order to reduce the redundancy of the fMRI data and transform the fMRI data to the constructed features with more classification potential,a feature construction method based on the siamese network named as SANet(Siamese Network)is proposed.It engineered the brain regions features under multiple scanning points of an fMRI image.The improved AlexNet is used for feature engineering,and the incremental feature selection strategy is used to find the best feature subset for the encephalopathy prediction task.The effects of three different network structures and four classifiers on the SANet model are evaluated for their prediction efficiencies,and the ablation experiment is conducted to verify the classification effect of the incremental feature selection algorithm on the SANet features.The experimental data shows that the SANet model can construct features from the fMRI data effectively,and improve the classification performance of original features.