id: 05958872
dt: a
an: 05958872
au: Chaves, R.; Ramírez, J.; Górriz, J.M.; Illán, I.; Segovia, F.; Olivares,
    A.
ti: Effective diagnosis of Alzheimer’s disease by means of distance metric
    learning and random forest.
so: Ferrández, José Manuel (ed.) et al., New challenges on bioinspired
    applications. 4th international work-conference on the interplay
    between natural and artificial computation, IWINAC 2011, La Palma,
    Canary Islands, Spain, May 30 ‒ June 3, 2011. Proceedings, Part II.
    Berlin: Springer (ISBN 978-3-642-21325-0/pbk). Lecture Notes in
    Computer Science 6687, 59-67 (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: SPECT Brain Imaging; Alzheimer’s disease; Distance Metric Learning;
    Kernel Principal Components Analysis; Random Forest; Support Vector
    Machines
ci: 
li: doi:10.1007/978-3-642-21326-7_7
ab: Summary: In this paper we present a novel classification method of SPECT
    images for the development of a computer aided diagnosis (CAD) system
    aiming to improve the early detection of the Alzheimer’s Disease
    (AD). The system combines firstly template-based normalized mean square
    error (NMSE) features of tridimensional Regions of Interest (ROIs)
    t-test selected with secondly Kernel Principal Components Analysis
    (KPCA) to find the main features. Thirdly, aiming to separate examples
    from different classes (Controls and ATD) by a Large Margin Nearest
    Neighbors technique (LMNN), distance metric learning methods namely
    Mahalanobis and Euclidean distances are used. Moreover, the proposed
    system evaluates Random Forests (RF) classifier, yielding a 98.97\% AD
    diagnosis accuracy, which reports clear improvements over existing
    techniques, for instance the Principal Component Analysis (PCA) or
    Normalized Minimum Squared Error (NMSE) evaluated with RF.
rv: