The nature of statistical learning theory
The nature of statistical learning theory
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Feature selection for the SVM: An application to hypertension diagnosis
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
Expert Systems with Applications: An International Journal
HealthAgents: distributed multi-agent brain tumor diagnosis and prognosis
Applied Intelligence
Feature Selection with Single-Layer Perceptrons for a Multicentre 1H-MRS Brain Tumour Database
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
Proceedings of the 29th DAGM conference on Pattern recognition
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part III
Expert Systems with Applications: An International Journal
Gene selection and classification of human lymphoma from microarray data
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
Artificial Intelligence in Medicine
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There has been a growing research interest in brain tumor classification based on proton magnetic resonance spectroscopy (^1H MRS) signals. Four research centers within the EU funded INTERPRET project have acquired a significant number of long echo ^1H MRS signals for brain tumor classification. In this paper, we present an objective comparison of several classification techniques applied to the discrimination of four types of brain tumors: meningiomas, glioblastomas, astrocytomas grade II and metastases. Linear and non-linear classifiers are compared: linear discriminant analysis (LDA), support vector machines (SVM) and least squares SVM (LS-SVM) with a linear kernel as linear techniques and LS-SVM with a radial basis function (RBF) kernel as a non-linear technique. Kernel-based methods can perform well in processing high dimensional data. This motivates the inclusion of SVM and LS-SVM in this study. The analysis includes optimal input variable selection, (hyper-) parameter estimation, followed by performance evaluation. The classification performance is evaluated over 200 stratified random samplings of the dataset into training and test sets. Receiver operating characteristic (ROC) curve analysis measures the performance of binary classification, while for multiclass classification, we consider the accuracy as performance measure. Based on the complete magnitude spectra, automated binary classifiers are able to reach an area under the ROC curve (AUC) of more than 0.9 except for the hard case glioblastomas versus metastases. Although, based on the available long echo ^1H MRS data, we did not find any statistically significant difference between the performances of LDA and the kernel-based methods, the latter have the strength that no dimensionality reduction is required to obtain such a high performance.