Machine Learning
Effective Emission Tomography Image Reconstruction Algorithms for SPECT Data
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
Cluster analysis of genome-wide expression data for feature extraction
Expert Systems with Applications: An International Journal
18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosis
Information Sciences: an International Journal
WBCD breast cancer database classification applying artificial metaplasticity neural network
Expert Systems with Applications: An International Journal
Brain status data analysis by sliding EMD
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Computer Aided Diagnosis tool for Alzheimer's Disease based on Mann-Whitney-Wilcoxon U-Test
Expert Systems with Applications: An International Journal
Deformation based feature selection for Computer Aided Diagnosis of Alzheimer's Disease
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper we propose a novel method for brain SPECT image feature extraction based on the empirical mode decomposition (EMD). The proposed method applied to assist the diagnosis of Alzheimer Disease (AD) selects the most discriminant voxels for support vector machine (SVM) classification from the transformed EMD feature space. In particular, the combination of frequency components of the EMD transformation are found to retain regional differences in functional activity which is characteristic of AD. In general, the EMD represents a fully data-driven, unsupervised and additive signal decomposition and does not need any a priori defined basis system. Several experiments were carried out on a balanced SPECT database collected from the ''Virgen de las Nieves'' Hospital in Granada (Spain), containing 96 recordings and yielding up to 100% maximum accuracy and 93.52+/-4.92% on average, with a acceptable biased estimate of the cross-validation (CV) true error, in separating AD and normal controls on this SPECT database. In this way, we achieve the ''gold standard'' labeling outperforming recently proposed CAD systems.