Machine Learning
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
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.