基于卷积循环神经网络的阿尔茨海默症早期诊断

Early diagnosis of Alzheimer′s disease based on convolutional recurrent neural network

早期准确诊断能延迟阿尔茨海默症(Alzheimer′s disease,AD)病情的恶化。磁共振成像(MRI)和正电子发射断层扫描(PET)已被证明有助于了解AD相关的解剖和功能性神经变化。近期研究表明,多模态特征的融合可以提高分类性能。本研究提出了一种基于卷积循环神经网络的多模态数据分类新框架,新框架结合了2D卷积神经网络和循环神经网络,以学习3D MRI和3D PET图像切分为2D切片序列之后的切片内、切片间特征,完成AD的早期诊断。本研究方法在AD与NC的分类实验中ACC为93.3%,AUC为98.1%;在MCIc与NC的分类实验准确率为83.8%,AUC为91.9%;MCIc与MCInc的分类实验准确率为79.0%,AUC为88.9%。结果表明该方法具有良好的分类性能。

Early and accurate diagnosis can delay the deterioration of the Alzheimer′s disease.Structural and functional neuroimaging images such as magnetic resonance imaging(MRI)and positron emission tomography(PET)have been shown to be useful in understanding AD-related anatomical and functional neurological changes.We proposed a new framework for multi-modal data classification based on convolutional recurrent neural networks.It combined 2D convolutional neural network(CNN)and recurrent neural network(RNN)to learn 3D MRI and 3D PET images into 2D slice sequences and slice features,to complete early diagnosis of AD.In the classification experiment of AD and NC,the ACC and AUC of this method were 93.3%,98.1%。The accuracy of the classification experiments in MCIc and NC was 83.8%,the AUC was 91.9%.The accuracy of the classification experiments in MCIc and MCInc was 79.0%,the AUC was 88.9%.The results show that this method has good classification performance.