使用深度学习与海马体异构特征融合的阿尔茨海默病分类方法

Method on Alzheimer’s Disease Classification Utilizing Deep Learning and Hippocampus Heterogeneous Feature Fusion

阿尔茨海默病(Alzheimer’s Disease,AD)是一种目前尚无有效方法治愈的神经系统退行性疾病,其准确分类有助于在AD早期阶段及时采取针对性治疗和干预措施,从而降低AD发病率和延缓AD疾病进展.本文提出一种使用深度学习和异构特征融合的AD分类新方法.针对大脑中的海马体结构,首先构建三维轻量级多分支注意力网络(Three-Dimensional Lightweight Multi-Branch Attention Net-work,3D-LMBAN)提取海马体深度特征;然后设计结合双树复小波变换(Dual-Tree Complex Wavelet Transform,DTCWT)和灰度游程矩阵(Gray-Level RunLength Matrix,GLRLM)的三维多尺度纹理特征提取方法提取海马体纹理特征;再使用传统方法提取海马体体积和形状特征;最后构建异构特征融合网络对提取得到的多种海马体特征进行降维表示、拼接和融合,进而实现AD分类.在EADC-ADNI数据集上进行实验,本文提出的AD分类方法的准确率(ACC)为93.39%,F1分数为93.10%,AUC为93.21%.实验结果表明本文提出的AD分类方法是有效的,且其性能优于传统的AD分类方法.

Alzheimer's Disease(AD)is a neurodegenerative disease that is currently incurable.Its accurate classification is advantageous to timely treatment and intervention at the early stage of AD,so as to reduce the incidence rate of AD and delay its progress.In this paper,one novel AD classification method utilizing deep learning and heterogeneous feature fusion is proposed.For the hippocampal structure in the brain,the three-dimensional lightweight multi-branch attention network(3D-LMBAN)is firstly constructed to extract the hippocampal depth features.Next,the three-dimensional multiscale texture feature extraction method combining dual-tree complex wavelet transform(DTCWT)and gray-level runlength matrix(GLRLM)is proposed to extract hippocampal texture features.Then,the hippocampal volume and shape features are extracted by conventional methods.Finally,the dimension-reduction representation,concatenation and fusion of extracted various hippocampal features are performed using the constructed heterogeneous feature fusion network,and then AD classification is realized.The proposed AD classification method is evaluated on the EADC-ADNI dataset.The accuracy(ACC),F1 score and AUC of proposed AD classification method are 93.39%,93.10%and 93.21%,respectively.The experimental results show that the proposed AD classification method is effective and better than other conventional AD classification methods.