Using machine learning techniques and brain MRI scans for detection of Alzheimer’s disease

Liu, Yuyang; Mazumdar, Suvodeep; Bath, Peter

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https://hdl.handle.net/2142/113739 Copy

Description

Title

Using machine learning techniques and brain MRI scans for detection of Alzheimer’s disease

Author(s)

• Liu, Yuyang
• Mazumdar, Suvodeep
• Bath, Peter

Issue Date

2022-02-28

Keyword(s)

• Alzheimer’s Disease
• Machine Learning
• MRI

Date of Ingest

2022-04-22T20:16:34Z

Abstract

Dementia is a clinical syndrome characterized by cognitive and behavioral impairment: it mostly affects people who are aged 65 years and over. Dementia results from several diseases, of which Alzheimer’s disease (AD) accounts for up to 80% of all dementia diagnoses. Magnetic Resonance Imaging (MRI) is one of the most widely used methods to diagnose AD but due to low efficiency of manual analysis, machine learning algorithms have been developed to diagnose AD using medical imaging data. In this study, unsupervised learning strategies were used to cluster the two diagnostic status, a healthy status called cognitively normal (CN), and AD, using brain structural MRI scans. First, we detected the abnormal regions between CN and AD using two-sample t-tests, and then employed an unsupervised learning neural network to extract features from brain MRI images. In the final stage, unsupervised learning (clustering) was implemented to discriminate between CN and AD data based on the extracted features. The approach was tested on 429 individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) who had baseline brain structural MRI scans: 231 CN and 198 AD. In the study, we found that the abnormal regions around the hippocampus were indicated based on two-sample t-test (p<0.0001), and the proposed methods using the abnormal regions yield the clustering results for CN vs. AD (accuracy=0.8163, specificity=0.7863, sensitivity=0.8436, and precision=0.8411 [mean values based on 10 runs]).

Publisher

iSchools

Type of Resource

Other

Genre of Resource

Conference Poster

Language

eng

Permalink

http://hdl.handle.net/2142/113739

Copyright and License Information

Copyright 2022 is held by Yuyang Liu, Suvodeep Mazumdar, and Peter Bath. Copyright permissions, when appropriate, must be obtained directly from the authors.

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