RT Journal Article
A1 Salim Lahmiri
AD Department of Computer Science, University of Quebec at Montreal, Montreal, Quebec, H3C 3P8, Canada
A1 Mounir Boukadoum
AD Department of Computer Science, University of Quebec at Montreal, Montreal, Quebec, H3C 3P8, Canada

PB iet
T1 New approach for automatic classification of Alzheimer's disease, mild cognitive impairment and healthy brain magnetic resonance images
JN Healthcare Technology Letters
VO 1
IS 1
SP 32
OP 36
AB 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.
K1 fractal object
K1 healthy brain image classification
K1 Hurst exponents
K1 fractal multiscale analysis
K1 AD classification
K1 Alzheimer disease
K1 cross-validation technique
K1 SVM
K1 clinical applications
K1 healthy brain magnetic resonance images
K1 support vector machines
K1 MCI
K1 mild cognitive impairment
DO https://doi.org/10.1049/htl.2013.0022
UL https://digital-library.theiet.org/;jsessionid=4ehg486qcnuss.x-iet-live-01content/journals/10.1049/htl.2013.0022
LA English
SN
YR 2014
OL EN