Early multidomain intervention to stave off Alzheimer&amp;rsquo;s disease

He, Rong-Qiao

doi:10.1360/n972016-00100

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…Alzheimer's disease is an incurable degenerative disease of the nervous system with insidious onset and chronic progression, mainly manifested in memory impairment, cognitive impairment, visual impairment and executive dysfunction, etc. [1][2][3][4][5][6]. Fortunately, with the continuous development of the magnetic resonance imaging (MRI) technology, MRI images have become a necessary tool for observation of brain activities and diagnosis of brain diseases [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

An Alzheimer’s Disease Identification and Classification Model Based on the Convolutional Neural Network with Attention Mechanisms

Chen¹

2021

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MRI image analysis of brain regions based on deep learning can effectively reduce the workload of doctors in reading films and improve the accuracy of diagnosis. Therefore, deep learning models have great application prospects in the classification and prediction of Alzheimer’s patients and normal people. However, the existing research has ignored the correlation between small abnormalities in local brain regions and changes in brain tissues. To this end, this paper studies an Alzheimer’s disease identification and classification model based on the convolutional neural network (CNN) with attention mechanisms. In this paper, the attention mechanisms were introduced from the regional level and the feature level, and the information of brain MRI images was fused from multiple levels to find out the correlation between the slices in brain MRI images. Then, a spatio-temporal graph CNN with dual attention mechanisms was constructed, which made the network model more attentive to the salient channel features while eliminating the impact of certain noise features. The experimental results verified the effectiveness of the constructed model in identification and classification of Alzheimer’s disease.

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