The aim of this paper is to build a tool that able to extract the regions from a brain magnetic resonance image that discriminate healthy controls from subjects with probable dementia of the Alzheimer type. We propose the use of an Extreme Learning Machine method to select the most discriminant regions and thereafter to perform the final classification according to a majority vote decision based strategy. We are selecting the optimal number of votes required to put a subject into the class “Alzheimer” by maximizing the global accuracy and minimizing the number of false positives. The discriminative regions selected in the case study are located in the hippocampus, amygdala, thalamus and putamen, among others. These locations are closely related with a Alzheimer disease according to the medical literature.
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Termenon, M., Graña, M., Savio, A., Akusok, A., Miche, Y., Björk, K-M., & Lendasse, A. (2016). Brain MRI morphological patterns extraction tool based on Extreme Learning Machine and majority vote classification. Neurocomputing, 174, Part A(January ), 344-351. https://doi.org/10.1016/j.neucom.2015.03.111