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Neuroimaging and Pattern Recognition Techniques for Automatic Detection of Alzheimer's Disease: A Review


Affiliations
1 Department of Electronics and Telecommunication Engineering, College of Engineering, Pune, India
2 Department of Electronics and Telecommunication Engineering, P.E.S's Modern College of Engineering, India
     

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Alzheimer's disease (AD) is the most common form of dementia with currently unavailable firm treatments that can stop or reverse the disease progression. A combination of brain imaging and clinical tests for checking the signs of memory impairment is used to identify patients with AD. In recent years, Neuroimaging techniques combined with machine learning algorithms have received lot of attention in this field. There is a need for development of automated techniques to detect the disease well before patient suffers from irreversible loss. This paper is about the review of such semi or fully automatic techniques with detail comparison of methods implemented, class labels considered, data base used and the results obtained for related study. This review provides detailed comparison of different Neuroimaging techniques and reveals potential application of machine learning algorithms in medical image analysis; particularly in AD enabling even the early detection of the disease- the class labelled as Multiple Cognitive Impairment.

Keywords

Image Classification, Feature Extraction, Computer Aided Diagnosis, Image Databases, Image Analysis, Alzheimer’s Disease.
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  • Neuroimaging and Pattern Recognition Techniques for Automatic Detection of Alzheimer's Disease: A Review

Abstract Views: 329  |  PDF Views: 5

Authors

Rupali Kamathe
Department of Electronics and Telecommunication Engineering, College of Engineering, Pune, India
Kalyani Joshi
Department of Electronics and Telecommunication Engineering, P.E.S's Modern College of Engineering, India

Abstract


Alzheimer's disease (AD) is the most common form of dementia with currently unavailable firm treatments that can stop or reverse the disease progression. A combination of brain imaging and clinical tests for checking the signs of memory impairment is used to identify patients with AD. In recent years, Neuroimaging techniques combined with machine learning algorithms have received lot of attention in this field. There is a need for development of automated techniques to detect the disease well before patient suffers from irreversible loss. This paper is about the review of such semi or fully automatic techniques with detail comparison of methods implemented, class labels considered, data base used and the results obtained for related study. This review provides detailed comparison of different Neuroimaging techniques and reveals potential application of machine learning algorithms in medical image analysis; particularly in AD enabling even the early detection of the disease- the class labelled as Multiple Cognitive Impairment.

Keywords


Image Classification, Feature Extraction, Computer Aided Diagnosis, Image Databases, Image Analysis, Alzheimer’s Disease.

References