Journal
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
Volume 29, Issue 2, Pages 121-131Publisher
WILEY
DOI: 10.1002/ima.22304
Keywords
Alzheimer's disease; downsized kernel principal component analysis; medical image diagnosis; mksvm; multiobjective optimization
Funding
- Open Access Series of Imaging Studies (OASIS) [U24 RR021382, P20 MH071616, R01 AG021910, P01 AG03991, P50 AG05681]
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Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness that cannot be cured, but the early diagnosis allows precautionary measures to be taken. The current used methods to detect Alzheimer's disease are based on tests of cognitive impairment, which does not provide an exact diagnosis before the patient passes a moderate stage of AD. In this article, a novel classifier of brain magnetic resonance images (MRI) based on the new downsized kernel principal component analysis (DKPCA) and multiclass support vector machine (SVM) is proposed. The suggested scheme classifies AD MRIs. First, a multiobjective optimization technique is used to determine the optimal parameter of the kernel function in order to ensure good classification results and to minimize the number of retained principle components simultaneously. The optimal parameter is used to build the optimized DKPCA model. Second, DKPCA is applied to normalized features. Downsized features are then fed to the classifier to output the prediction. To validate the effectiveness of the proposed method, DKPCA was tested using synthetic data to demonstrate its efficiency on dimensionality reduction, then the DKPCA based technique was tested on the OASIS MRI database and the results were satisfactory compared to conventional approaches.
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