Multiple-Activation Parallel Convolution Network in Combination with t-SNE for the Classification of Mild Cognitive Impairment

Harsh Bhasin, R. Agrawal
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Abstract

The classification of Mild Cognitive Impairment can be done using 2-D CNN, which take a single slice at a time as input and do not consider pixel information from adjacent slices or spatial correlation amongst the slices of the brain volume or 3-D CNN, which requires huge computation time and memory as a significantly large number of parameters involved in 3D-CNN in comparison to 2D-CNN. To reduce the spatial correlation, computational complexity, and memory requirement, we use t-Distributed Stochastic Neighbor Embedding (t-SNE) on MRI volume to reduce its dimensions. Also, we use parallel CNN instead of sequential to analyze MRI volumes and a combination of RELU, sigmoid, and SIREN activation functions to learn better features for the classification of MCI. To check the efficacy of the proposed t-SNE Multiple-Activation Parallel Convolution Network, experiments are performed on publicly available Alzheimer's Disease Neuroimaging Initiative dataset, and performance is compared with existing methods. We obtain classification accuracy of 94.15 and 94.89 on MCI-C Vs. MCI-NC data and MCI Vs. Controls data respectively.
多重激活并行卷积网络联合t-SNE对轻度认知障碍的分类
轻度认知障碍的分类可以使用二维CNN进行,二维CNN每次只输入一个切片,不考虑相邻切片的像素信息,也不考虑脑容量或三维CNN切片之间的空间相关性,与2D-CNN相比,3D-CNN涉及的参数数量明显更多,需要大量的计算时间和内存。为了降低空间相关性、计算复杂度和内存需求,我们在MRI体积上使用t-分布随机邻居嵌入(t-SNE)来降低其维数。此外,我们使用并行CNN代替顺序CNN来分析MRI体积,并结合RELU、sigmoid和SIREN激活函数来学习更好的MCI分类特征。为了验证所提出的t-SNE多激活并行卷积网络的有效性,在公开的阿尔茨海默病神经成像倡议数据集上进行了实验,并与现有方法进行了性能比较。我们在MCI- c与MCI- nc数据和MCI与Controls数据上分别获得了94.15和94.89的分类准确率。
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