3DPCANet在阿尔茨海默症功能磁共振成像图像分类中的应用

阿尔茨海默症(AD)是一种起病隐匿的进行性神经退行性疾病,会使患者的大脑脑区结构发生改变.为辅助医生对AD患者的病情做出正确判断,提出了一种改进的三维主成分分析网络(3DPCANet)模型,并结合被试者全脑均值低频波动振幅(mALFF)图像来对AD进行分类.首先,对功能磁共振成像(fMRI)数据进行预处理,计算出全脑m...

Application of 3DPCANet in image classification of functional magnetic resonance imaging for Alzheimer's disease

Alzheimer’s Disease （AD） is a progressive neurodegenerative disease with hidden causes， and can result in structural changes of patients’ brain regions. For assisting the doctors to make correct judgment on the condition of AD patients， an improved Three-Dimensional Principal Component Analysis Network （3DPCANet） model was proposed to classify AD by combining the mean Amplitude of Low-Frequency Fluctuation （mALFF） image of the whole brain of the subject. Firstly， functional Magnetic Resonance Imaging （fMRI） data were preprocessed， and the mALFF image of the whole brain was calculated. Then， the improved 3DPCANet deep learning model was used for feature extraction. Finally， Support Vector Machine （SVM） was used to classify features of AD patients with different stages. Experimental results show that the proposed model is simple and robust， and has the classification accuracies on Subjective Memory Decline （SMD） vs. AD， SMD vs. Late Mild Cognitive Impairment （LMCI）， and LMCI vs. AD reached 92.42%， 91.80% and 89.33% respectively， which verifies the effectiveness and feasibility of the proposed method.