Boosting diagnosis accuracy of Alzheimer's disease using high dimensional recognition of longitudinal brain atrophy patterns

Behavioural Brain Research

Volumn 290, Issue , 2015, Pages 124-130

  Farzan, Ali ^a   Mashohor, Syansiah ^b,c   Ramli, Abd Rahman ^b   Mahmud, Rozi ^b  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

^b UNIVERSITY PUTRA MALAYSIA   (Malaysia)

^c Institute of Advanced Technology   (Malaysia)

Author keywords

Alzheimer's disease; Diagnostic; FCM; Longitudinal atrophy; MRI; SVM

Indexed keywords

AGED; ALZHEIMER DISEASE; ARTICLE; BRAIN SIZE; CLASSIFICATION ALGORITHM; CLASSIFIER; CLINICAL ARTICLE; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; FEMALE; FOLLOW UP; FUZZY CLUSTERING METHOD; HUMAN; KERNEL METHOD; LONGITUDINAL BRAIN ATROPHY PATTERN; MACHINE LEARNING; MALE; MATHEMATICAL COMPUTING; NERVOUS SYSTEM PARAMETERS; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; RADIAL BASED FUNCTION; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; VALIDATION STUDY; ATROPHY; BRAIN; CLUSTER ANALYSIS; PATHOLOGY; PROCEDURES;

AGED; ALZHEIMER DISEASE; ATROPHY; BRAIN; CLUSTER ANALYSIS; FEMALE; FOLLOW-UP STUDIES; HUMANS; MAGNETIC RESONANCE IMAGING; MALE; PRINCIPAL COMPONENT ANALYSIS; SUPPORT VECTOR MACHINE;

EID: 84929379453     PISSN: 01664328     EISSN: 18727549     Source Type: Journal    
DOI: 10.1016/j.bbr.2015.04.010     Document Type: Article

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