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Open AccessArticle

Multi-View Based Multi-Model Learning for MCI Diagnosis

School of Computer Science and Engineering, Central South University, Changsha 410083, China
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Brain Sci. 2020, 10(3), 181; https://doi.org/10.3390/brainsci10030181
Received: 3 March 2020 / Accepted: 16 March 2020 / Published: 20 March 2020
(This article belongs to the Special Issue Early Diagnosis and Intervention in Alzheimer's Disease)
Mild cognitive impairment (MCI) is the early stage of Alzheimer’s disease (AD). Automatic diagnosis of MCI by magnetic resonance imaging (MRI) images has been the focus of research in recent years. Furthermore, deep learning models based on 2D view and 3D view have been widely used in the diagnosis of MCI. The deep learning architecture can capture anatomical changes in the brain from MRI scans to extract the underlying features of brain disease. In this paper, we propose a multi-view based multi-model (MVMM) learning framework, which effectively combines the local information of 2D images with the global information of 3D images. First, we select some 2D slices from MRI images and extract the features representing 2D local information. Then, we combine them with the features representing 3D global information learned from 3D images to train the MVMM learning framework. We evaluate our model on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our proposed model can effectively recognize MCI through MRI images (accuracy of 87.50% for MCI/HC and accuracy of 83.18% for MCI/AD). View Full-Text
Keywords: alzheimer’s disease; magnetic resonance imaging; multi-view; CNN alzheimer’s disease; magnetic resonance imaging; multi-view; CNN
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Cao, P.; Gao, J.; Zhang, Z. Multi-View Based Multi-Model Learning for MCI Diagnosis. Brain Sci. 2020, 10, 181.

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