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 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.
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Chaves, R., Ramírez, J., Górriz, J.M., Illán, I., Segovia, F., Olivares, A. (2011). Effective Diagnosis of Alzheimer’s Disease by Means of Distance Metric Learning and Random Forest. 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_7
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DOI: https://doi.org/10.1007/978-3-642-21326-7_7
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