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With the development of artificial intelligence, computer-aided diagnosis plays an increasingly important role in Alzheimer’s disease (AD). In this paper, a new multiclassification diagnostic algorithm based on TOP-MRI images and clinical indicators is proposed. The features of TOP-MRI images and clinical indicators are fully exploited for multiclassification diagnosis of AD. First, we design TOP-CNN-NN model based on three VGGNet-16 convolutional neural networks and a single hidden layer neural network to extract the image feature vector of brain three orthogonal planes (TOP) MRI images. Then we screen clinical data using CfsSubsetEval evaluator to compose clinical feature vector. Then, the image feature vector and indicator feature vector are fused by using linear fusion method of multi-source data based on canonical correlation analysis (CCA). Finally, the fusion vector becomes the input for multi-classification classifier to distinguish three stages of AD: control normal (CN), mild cognitive impairment (MCI) and Alzheimer's disease (AD). The proposed algorithm is validated using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Experiments show a good performance since the accuracy of the proposed algorithm in the multi-classification of AD can reach 86.7%. Keywords— Alzheimer’s disease; linear fusion; multi-source data; multi-classification; deep learning

Multi Classification of Alzheimer's Disease using Linear Fusion with TOP-MRI Images and Clinical Indicators