基于卷积神经网络的注意缺陷多动障碍分类研究

Study of attention deficit/hyperactivity disorder classification based on convolutional neural networks

注意缺陷多动障碍(ADHD)是一种高发于学龄儿童的行为障碍综合症。目前,ADHD的诊断主要依赖主观方法,导致漏诊率和误诊率较高。基于此,本文提出一种基于卷积神经网络的ADHD客观分类算法。首先,对脑部磁共振图像(MRI)进行头骨剥离、高斯核平滑等预处理;其次,对大脑的右侧尾状核、左侧楔前叶和左侧额上回部位的MRI进行粗分割;最后,利用3层卷积神经网络进行分类。实验结果表明:1本文的算法能有效地对ADHD和正常人群进行分类;2右侧尾状核和左侧楔前叶的ADHD分类准确率要高于ADHD-200全球竞赛中所有方法达到的ADHD最高分类准确率(62.52%);3利用上述3个脑区对ADHD患者和正常人群进行分类,其中右侧尾状核的分类准确率最高。综上所述,本文提出了一种利用粗分割和深度学习对ADHD患者和正常人群进行分类的方法。本文方法分类准确率高,计算量小,能较好地提取不明显的图像特征,改善了传统MRI脑区精确分割耗时长及复杂度高的缺点,为ADHD的诊断提供了一种可参照的客观方法。

Attention deficit/hyperactivity disorder （ADHD） is a behavioral disorder syndrome found mainly in school-age population. At present, the diagnosis of ADHD mainly depends on the subjective methods, leading to the high rate of misdiagnosis and missed-diagnosis. To solve these problems, we proposed an algorithm for classifying ADHD objectively based on convolutional neural network. At first, preprocessing steps, including skull stripping, Gaussian kernel smoothing, et al., were applied to brain magnetic resonance imaging （MRI）. Then, coarse segmentation was used for selecting the right caudate nucleus, left precuneus, and left superior frontal gyrus region. Finally, a 3 level convolutional neural network was used for classification. Experimental results showed that the proposed algorithm was capable of classifying ADHD and normal groups effectively, the classification accuracies obtained by the right caudate nucleus and the left precuneus brain regions were greater than the highest classification accuracy （62.52%） in the ADHD-200 competition, and among 3 brain regions in ADHD and the normal groups, the classification accuracy from the right caudate nucleus was the highest. It is well concluded that the method for classification of ADHD and normal groups proposed in this paper utilizing the coarse segmentation and deep learning is a useful method for the purpose. The classification accuracy of the proposed method is high, and the calculation is simple. And the method is able to extract the unobvious image features better, and can overcome the shortcomings of traditional methods of MRI brain area segmentation, which are time-consuming and highly complicate. The method provides an objective diagnosis approach for ADHD.