Automated Detection of Alzheimer’s Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques

The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer’s disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure...

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Veröffentlicht in:Journal of medical systems Jg. 43; H. 9; S. 302 - 14
Hauptverfasser: Acharya, U. Rajendra, Fernandes, Steven Lawrence, WeiKoh, Joel En, Ciaccio, Edward J., Fabell, Mohd Kamil Mohd, Tanik, U. John, Rajinikanth, V., Yeong, Chai Hong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.09.2019
Springer Nature B.V
Schlagworte:
Abnormalities
Alzheimer Disease - classification
Alzheimer Disease - diagnostic imaging
Alzheimer's disease
Brain
Brain - pathology
Classification
Cognitive ability
Comparative analysis
Diagnosis
Diagnosis, Computer-Assisted - methods
Feature extraction
Health Informatics
Health Informatics and Computer Vision
Health Sciences
Humans
Image & Signal Processing
Image acquisition
Image detection
Image Interpretation, Computer-Assisted - methods
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Medical imaging
Medicine
Medicine & Public Health
Neuroimaging
NMR
Nuclear magnetic resonance
Pattern Recognition, Automated - methods
Recent Advances in Deep Learning for Biomedical Signal Processing
Sensitivity
Series (mathematics)
Statistics for Life Sciences
Performance evaluation
Feature extraction
KNN classifier
Brain MRI
Alzheimer’s disease
ISSN:0148-5598, 1573-689X, 1573-689X
Online-Zugang:Volltext
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Zusammenfassung:The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer’s disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure, widely adopted in hospitals to examine cognitive abnormalities. Images are acquired using the T2 imaging sequence. The paradigm consists of a series of quantitative techniques: filtering, feature extraction, Student’s t-test based feature selection, and k-Nearest Neighbor (KNN) based classification. Additionally, a comparative analysis is done by implementing other feature extraction procedures that are described in the literature. Our findings suggest that the Shearlet Transform (ST) feature extraction technique offers improved results for Alzheimer’s diagnosis as compared to alternative methods. The proposed CABD tool with the ST + KNN technique provided accuracy of 94.54%, precision of 88.33%, sensitivity of 96.30% and specificity of 93.64%. Furthermore, this tool also offered an accuracy, precision, sensitivity and specificity of 98.48%, 100%, 96.97% and 100%, respectively, with the benchmark MRI database.
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ISSN:0148-5598
1573-689X
1573-689X
DOI:10.1007/s10916-019-1428-9