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. 2021 Sep 9;11(9):902.
doi: 10.3390/jpm11090902.

MRI Deep Learning-Based Solution for Alzheimer's Disease Prediction

Cristina L Saratxaga  1 , Iratxe Moya  2 , Artzai Picón  1 , Marina Acosta  2 , Aitor Moreno-Fernandez-de-Leceta  2 , Estibaliz Garrote  1   3 , Arantza Bereciartua-Perez  1
Affiliations

MRI Deep Learning-Based Solution for Alzheimer's Disease Prediction

Cristina L Saratxaga et al. J Pers Med. .

Abstract

Background: Alzheimer's is a degenerative dementing disorder that starts with a mild memory impairment and progresses to a total loss of mental and physical faculties. The sooner the diagnosis is made, the better for the patient, as preventive actions and treatment can be started. Although tests such as the Mini-Mental State Tests Examination are usually used for early identification, diagnosis relies on magnetic resonance imaging (MRI) brain analysis.

Methods: Public initiatives such as the OASIS (Open Access Series of Imaging Studies) collection provide neuroimaging datasets openly available for research purposes. In this work, a new method based on deep learning and image processing techniques for MRI-based Alzheimer's diagnosis is proposed and compared with previous literature works.

Results: Our method achieves a balance accuracy (BAC) up to 0.93 for image-based automated diagnosis of the disease, and a BAC of 0.88 for the establishment of the disease stage (healthy tissue, very mild and severe stage).

Conclusions: Results obtained surpassed the state-of-the-art proposals using the OASIS collection. This demonstrates that deep learning-based strategies are an effective tool for building a robust solution for Alzheimer's-assisted diagnosis based on MRI data.

Keywords: Alzheimer’s; MRI; OASIS; classification; deep learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure A1
Figure A1
Distribution of the dataset into its two principal components separated in color by clusters.
Figure A2
Figure A2
MRI volume prior to processing (upper row) and after skull removal processing (bottom row). Column A: coronal plane, column B: sagittal view and column C: axial plane.
Figure 1
Figure 1
BrainNet2D network (A), and BrainNet2D network with Batch Normalization (B).
Figure 2
Figure 2
BrainNet3D network (A), and BrainNet3D network with Batch Normalization (B).
See this image and copyright information in PMC

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