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OACSCDCSTPCD

Alzheimer's Disease(AD)is a neurodegenerative disease that is currently incurable.Its accurate classifi-cation 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 fea-ture 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 multi-scale texture feature extraction method combining dual-tree complex wavelet transform(DTCWT)and gray-level run-length matrix(GLRLM)is proposed to extract hippocampal texture features.Then,the hippocampal volume and shape fea-tures 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 accura-cy(ACC),F1 score and AUC of proposed AD classification method are 93.39%,93.10%and 93.21%,respectively.The ex-perimental results show that the proposed AD classification method is effective and better than other conventional AD clas-sification methods.