Alzheimer disease classification by combination of SRAN, Linear SVM, PCA, and VBM
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초록
Early accurate detection of Alzheimer disease (AD) and its prognostic stage, i.e., Mild Cognitive Impairment (MCI) is getting more and more vital. The preventive measure could presumably treat to get rid of Alzheimer disease risk factors to generate. The bio-marker, cerebral atrophy could be observed in structural MR imaging (sMRI). Structural MRI imaging is used to extract morphometric features after Grey Matter (GM) segmentation. Finally a novel approach is applied for the diagnosis of very mild (CDR-0.5) to mild (CDR-1) alzheimer disease patients from normal controls combining morphometric features along with MMSE (Mini-Mental State Examination) score. The combined features are fed into recently proposed Self Adaptive Resource Allocation Network (SRAN) and Linear Support Vector Machine (L-SVM) classifier after getting rid of curse of dimensionality using principal component analysis. The experimental result of the proposed diagnosis support methods yield up to 95.83% stratification accuracy with Linear SVM and 83.33% with SRAN classifier along with high sensitivity and specificity above 90%.
Abstract
Early accurate detection of Alzheimer disease (AD) and its prognostic stage, i.e., Mild Cognitive Impairment (MCI) is getting more and more vital. The preventive measure could presumably treat to get rid of Alzheimer disease risk factors to generate. The bio-marker, cerebral atrophy could be observed in structural MR imaging (sMRI). Structural MRI imaging is used to extract morphometric features after Grey Matter (GM) segmentation. Finally a novel approach is applied for the diagnosis of very mild (CDR-0.5) to mild (CDR-1) alzheimer disease patients from normal controls combining morphometric features along with MMSE (Mini-Mental State Examination) score. The combined features are fed into recently proposed Self Adaptive Resource Allocation Network (SRAN) and Linear Support Vector Machine (L-SVM) classifier after getting rid of curse of dimensionality using principal component analysis. The experimental result of the proposed diagnosis support methods yield up to 95.83% stratification accuracy with Linear SVM and 83.33% with SRAN classifier along with high sensitivity and specificity above 90%.
참고문헌 (13)
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