Abstract
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 Large Margin Nearest Neighbors (LMNN), which is a distance metric technique aiming to separate examples from different classes (Controls and AD) by a Large Margin. LMNN uses a rectangular matrix (called RECT-LMNN) as an effective feature reduction technique. Moreover, the proposed system evaluates Support Vector Machine (SVM) classifier, yielding a 97.93% AD diagnosis accuracy, which reports clear improvements over existing techniques, for instance the Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) or Normalized Minimum Squared Error (NMSE) evaluated with SVM.
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Chaves, R., Ramírez, J., Górriz, J.M., Salas-Gonzalez, D., López, M. (2011). Distance Metric Learning as Feature Reduction Technique for the Alzheimer’s Disease Diagnosis. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) New Challenges on Bioinspired Applications. IWINAC 2011. Lecture Notes in Computer Science, vol 6687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21326-7_8
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DOI: https://doi.org/10.1007/978-3-642-21326-7_8
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