Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults

The Open Access Series of Imaging Studies is a series of magnetic resonance imaging data sets that is publicly available for study and analysis. The initial data set consists of a cross-sectional collection of 416 subjects aged 18 to 96 years. One hundred of the included subjects older than 60 years have been clinically diagnosed with very mild to moderate Alzheimer's disease. The subjects are all right-handed and include both men and women. For each subject, three or four individual T1-weighted magnetic resonance imaging scans obtained in single imaging sessions are included. Multiple within-session acquisitions provide extremely high contrast-to-noise ratio, making the data amenable to a wide range of analytic approaches including automated computational analysis. Additionally, a reliability data set is included containing 20 subjects without dementia imaged on a subsequent visit within 90 days of their initial session. Automated calculation of whole-brain volume and estimated total intracranial volume are presented to demonstrate use of the data for measuring differences associated with normal aging and Alzheimer's disease.

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Sensitivity and Specificity of Positron Emission Tomography and Magnetic Resonance Imaging Studies in Alzheimer’s Disease and Multi-Infarct Dementia

European Neurology ◽

10.1159/000116474 ◽

1989 ◽

Vol 29 (3) ◽

pp. 9-15 ◽

Cited By ~ 45

Author(s):

Ranjan Duara ◽

William Barker ◽

David Loewenstein ◽

Shlomo Pascal ◽

Brian Bowen

Keyword(s):

Magnetic Resonance Imaging ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Positron Emission Tomography ◽

Magnetic Resonance ◽

Sensitivity And Specificity ◽

Emission Tomography ◽

Resonance Imaging ◽

Imaging Studies ◽

Positron Emission

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White Matter Changes in Depression and Alzheimer's Disease: A Review of Magnetic Resonance Imaging Studies

International Journal of Geriatric Psychiatry ◽

10.1002/(sici)1099-1166(199608)11:8<681::aid-gps426>3.0.co;2-u ◽

1996 ◽

Vol 11 (8) ◽

pp. 681-694 ◽

Cited By ~ 46

Author(s):

JOHN T. O'BRIEN ◽

DAVID AMES ◽

ISAAC SCHWIETZER

Keyword(s):

Magnetic Resonance Imaging ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

White Matter ◽

Magnetic Resonance ◽

Resonance Imaging ◽

Imaging Studies ◽

White Matter Changes

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Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network

Applied Bionics and Biomechanics ◽

10.1155/2021/6690539 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Fanar E. K. Al-Khuzaie ◽

Oguz Bayat ◽

Adil D. Duru

Keyword(s):

Neural Network ◽

Magnetic Resonance Imaging ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Magnetic Resonance ◽

Convolutional Neural Network ◽

Dropout Rate ◽

Resonance Imaging ◽

Data Set ◽

The Brain

There are many kinds of brain abnormalities that cause changes in different parts of the brain. Alzheimer’s disease is a chronic condition that degenerates the cells of the brain leading to memory asthenia. Cognitive mental troubles such as forgetfulness and confusion are one of the most important features of Alzheimer’s patients. In the literature, several image processing techniques, as well as machine learning strategies, were introduced for the diagnosis of the disease. This study is aimed at recognizing the presence of Alzheimer’s disease based on the magnetic resonance imaging of the brain. We adopted a deep learning methodology for the discrimination between Alzheimer’s patients and healthy patients from 2D anatomical slices collected using magnetic resonance imaging. Most of the previous researches were based on the implementation of a 3D convolutional neural network, whereas we incorporated the usage of 2D slices as input to the convolutional neural network. The data set of this research was obtained from the OASIS website. We trained the convolutional neural network structure using the 2D slices to exhibit the deep network weightings that we named as the Alzheimer Network (AlzNet). The accuracy of our enhanced network was 99.30%. This work investigated the effects of many parameters on AlzNet, such as the number of layers, number of filters, and dropout rate. The results were interesting after using many performance metrics for evaluating the proposed AlzNet.

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