@ARTICLE{ iet:/content/journals/10.1049/htl.2013.0022, author = {Salim Lahmiri}, affiliation = {Department of Computer Science, University of Quebec at Montreal, Montreal, Quebec, H3C 3P8, Canada}, author = {Mounir Boukadoum}, affiliation = {Department of Computer Science, University of Quebec at Montreal, Montreal, Quebec, H3C 3P8, Canada}, keywords = {fractal object;healthy brain image classification;Hurst exponents;fractal multiscale analysis;AD classification;Alzheimer disease;cross-validation technique;SVM;clinical applications;healthy brain magnetic resonance images;support vector machines;MCI;mild cognitive impairment;}, language = {English}, abstract = {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.}, title = {New approach for automatic classification of Alzheimer's disease, mild cognitive impairment and healthy brain magnetic resonance images}, journal = {Healthcare Technology Letters}, issue = {1}, volume = {1}, year = {2014}, month = {March}, pages = {32-36(4)}, publisher ={Institution of Engineering and Technology}, copyright = {© The Institution of Engineering and Technology}, url = {https://digital-library.theiet.org/;jsessionid=c75dag2wqqcf.x-iet-live-01content/journals/10.1049/htl.2013.0022} }