The pathogenesis of Alzheimer's disease (AD), a common neurodegenerative disease, is still unknown. It is difficult to determine the atrophy areas, especially for patients with mild cognitive impairment (MCI) at different stages of AD, which results in a low diagnostic rate. Therefore, an early diagnosis model of AD based on 3-dimensional convolutional neural network (3DCNN) and genetic algorithm (GA) was proposed. Firstly, the 3DCNN was used to train a base classifier for each region of interest (ROI). And then, the optimal combination of the base classifiers was determined with the GA. Finally, the ensemble consisting of the chosen base classifiers was employed to make a diagnosis for a patient and the brain regions with significant classification capability were decided. The experimental results showed that the classification accuracy was 88.6% for AD vs. normal control (NC), 88.1% for MCI patients who will convert to AD (MCIc) vs. NC, and 71.3% for MCI patients who will not convert to AD (MCInc) vs. MCIc. In addition, with the statistical analysis of the behavioral domains corresponding to ROIs (i.e. brain regions), besides left hippocampus, medial and lateral amygdala, and left para-hippocampal gyrus, anterior superior temporal sulcus of middle temporal gyrus and dorsal area 23 of cingulate gyrus were also found with GA. It is concluded that the functions of the selected brain regions mainly are relevant to emotions, memory, cognition and the like, which is basically consistent with the symptoms of indifference, memory losses, mobility decreases and cognitive declines in AD patients. All of these show that the proposed method is effective.
阿尔茨海默病（AD）作为一种常见的神经系统退行性疾病，其致病机制不明，尤其是对处于 AD 不同阶段的轻度认知障碍（MCI）患者的萎缩区域难以确定，导致误诊率偏高。为此，提出了基于 3 维卷积神经网络（3DCNN）和遗传算法（GA）相结合的 AD 早期辅助诊断模型。首先用 3DCNN 针对感兴趣区域（ROI）训练出候选基分类器，然后利用 GA 算法从中挑选出最优基分类器组合，最后集成起来进行分类，实现辅助诊断。同时，由于基分类器与脑区之间是一一对应的，进而可以找出具有显著分类能力的脑区。实验结果表明，AD 与正常组（NC）的分类准确率为 88.6%，转化为 AD 的 MCI（MCIc）与 NC 的分类准确率为 88.1%，未转化为 AD 的 MCI（MCInc）与 MCIc 的分类准确率为 71.3%。此外，通过对关键 ROI（即脑区）所对应的行为域数据进行统计分析，GA 筛选的关键脑区除了左延髓海马、左尾部海马和内外侧杏仁核、左海马旁回，还新发现了右颞中回前颞上沟、右扣带回背侧 23 等区域。实验得出所选脑区的功能主要影响情绪、记忆和认知等方面，这与 AD 患者出现的感情冷淡、记忆力下降、行动能力下降和认知水平下降等外在表现基本吻合。这些均表明所提方法是有效的。.
Keywords: Alzheimer's disease; classification; convolutional neural network; genetic algorithm; region of interest.