Abstract
Automated structural magnetic resonance imaging (MRI) classification has gained popularity for the early detection of mild cognitive impairment (MCI), the first stage of dementia condition with an increased risk of eventually developing Alzheimer’s disease (AD). In general, an MRI diagnosis system requires some fundamental activities: MRI processing, features selection, data classification. The aim of this paper is twofold: (i) first, a high-performance classification algorithm based on particle-Bernstein polynomials (PBPs), recently proposed for nonlinear regression of input–output data that combines low complexity and good accuracy, has been developed, (ii) second, an MRI-based computer-aided diagnosis (CAD) system for the classification of AD has been derived. Several experiments on a dataset from Alzheimer’s Disease Neuroimaging Initiative (ADNI) and comparisons with the state-of-the-art establish the performance of the method.
Alzheimer’s Disease Neuroimaging Initiative—Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
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This work was supported by a Università Politecnica delle Marche Research Grant.
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Biagetti, G., Crippa, P., Falaschetti, L., Luzzi, S., Santarelli, R., Turchetti, C. (2019). Classification of Alzheimer’s Disease from Structural Magnetic Resonance Imaging using Particle-Bernstein Polynomials Algorithm. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2019. Smart Innovation, Systems and Technologies, vol 143. Springer, Singapore. https://doi.org/10.1007/978-981-13-8303-8_5
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