Department of Computer Engineering, University of Chosun, Gwangju 61452, Republic of Korea.
Sensors (Basel). 2023 Jul 12;23(14):6330. doi: 10.3390/s23146330.
In the last three decades, the development of functional magnetic resonance imaging (fMRI) has significantly contributed to the understanding of the brain, functional brain mapping, and resting-state brain networks. Given the recent successes of deep learning in various fields, we propose a 3D-CNN-LSTM classification model to diagnose health conditions with the following classes: condition normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer's disease (AD). The proposed method employs spatial and temporal feature extractors, wherein the former utilizes a U-Net architecture to extract spatial features, and the latter utilizes long short-term memory (LSTM) to extract temporal features. Prior to feature extraction, we performed four-step pre-processing to remove noise from the fMRI data. In the comparative experiments, we trained each of the three models by adjusting the time dimension. The network exhibited an average accuracy of 96.4% when using five-fold cross-validation. These results show that the proposed method has high potential for identifying the progression of Alzheimer's by analyzing 4D fMRI data.
在过去的三十年中，功能磁共振成像（fMRI）的发展极大地促进了对大脑、功能脑映射和静息态脑网络的理解。鉴于深度学习在各个领域的最新成功，我们提出了一个 3D-CNN-LSTM 分类模型，用于诊断以下健康状况类别：正常状态（CN）、早期轻度认知障碍（EMCI）、晚期轻度认知障碍（LMCI）和阿尔茨海默病（AD）。所提出的方法采用空间和时间特征提取器，其中前者利用 U-Net 架构提取空间特征，后者利用长短时记忆（LSTM）提取时间特征。在特征提取之前，我们对 fMRI 数据进行了四步预处理，以去除噪声。在对比实验中，我们通过调整时间维度来训练每个模型。使用五折交叉验证时，网络的平均准确率为 96.4%。这些结果表明，该方法通过分析 4D fMRI 数据，具有识别阿尔茨海默病进展的巨大潜力。
