Review on Alzheimer Disease Detection Methods: Automatic Pipelines and Machine Learning Techniques

Alzheimer’s Disease (AD) is becoming increasingly prevalent across the globe, and various diagnostic and detection methods have been developed in recent years. Several techniques are available, including Automatic Pipeline Methods and Machine Learning Methods that utilize Biomarker Methods, Fusion,...

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Bibliographic Details
Published in:Sci 2023-03, Vol.5 (1), p.13
Main Authors: Shukla, Amar, Tiwari, Rajeev, Tiwari, Shamik
Format: Article
Language:English
Subjects:
Alzheimer disease
Alzheimer's disease
automated pipeline methods
biomarker methods
Biomarkers
fusion based methods
machine learning methods
Magnetic resonance imaging
Neural networks
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Summary:Alzheimer’s Disease (AD) is becoming increasingly prevalent across the globe, and various diagnostic and detection methods have been developed in recent years. Several techniques are available, including Automatic Pipeline Methods and Machine Learning Methods that utilize Biomarker Methods, Fusion, and Registration for multimodality, to pre-process medical scans. The use of automated pipelines and machine learning systems has proven beneficial in accurately identifying AD and its stages, with a success rate of over 95% for single and binary class classifications. However, there are still challenges in multi-class classification, such as distinguishing between AD and MCI, as well as sub-stages of MCI. The research also emphasizes the significance of using multi-modality approaches for effective validation in detecting AD and its stages.
ISSN:2413-4155
2413-4155
DOI:10.3390/sci5010013