首页|New approach for automatic classification of Alzheimer's disease, mild cognitive impairment and healthy brain magnetic resonance images
New approach for automatic classification of Alzheimer's disease, mild cognitive impairment and healthy brain magnetic resonance images
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NETL
NSTL
Inst Engineering Technology-Iet
Explored is the utility of modelling brain magnetic resonance images as a fractal object for the classification of healthy brain images against those with Alzheimer's disease (AD) or mild cognitive impairment (MCI). More precisely, fractal multi-scale analysis is used to build feature vectors from the derived Hurst's exponents. These are then classified by support vector machines (SVMs). Three experiments were conducted: in the first the SVM was trained to classify AD against healthy images. In the second experiment, the SVM was trained to classify AD against MCI and, in the third experiment, a multiclass SVM was trained to classify all three types of images. The experimental results, using the 10-fold cross-validation technique, indicate that the SVM achieved 97.08\% ± 0.05 correct classification rate, 98.09\% ± 0.04 sensitivity and 96.07\% ± 0.07 specificity for the classification of healthy against MCI images, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved 97.5\% ± 0.04 correct classification rate, 100\% sensitivity and 94.93\% ± 0.08 specificity. The third experiment also showed that the multiclass SVM provided highly accurate classification results. The processing time for a given image was 25 s. These findings suggest that this approach is efficient and may be promising for clinical applications.
NETL
NSTL
Inst Engineering Technology-Iet
