Many approaches have been considered for automatic grading of brain tumors by means of
pattern recognition with magnetic resonance spectroscopy (MRS). Providing an improved …

Purpose To investigate whether nonlinear dimensionality reduction improves unsupervised
classification of 1H MRS brain tumor data compared with a linear method. Methods In vivo …

Machine Learning (ML) and related methods have of late made significant contributions to
solving multidisciplinary problems in the field of oncology diagnosis. Human brain tumor …

OBJECTIVE: The objective of this study was to determine whether metabolic parameters
derived from ex vivo analysis of tissue samples are predictive of biologic characteristics of …

Machine learning is a powerful paradigm to analyze Proton Magnetic Resonance
Spectroscopy 1H-MRS spectral data for the classification of brain tumor pathologies. An …

In cancer diagnosis, classification of the different tumor types is of great importance. An
accurate prediction of basic tumor types provides better treatment and may minimize the …

Abstract Machine learning is a powerful paradigm within which to analyze 1 H-MRS spectral
data for the classification of tumour pathologies. An important characteristic of this task is the …

In cancer diagnosis, classification of the different tumor types is of great importance. An
accurate prediction of different tumor types provides better treatment and toxicity …

The characterization of brain tumors involves the analysis of multiple heterogeneous data
sets that include various types of medical images and spectroscopy, clinical and …

In cancer diagnosis, classification of the different tumor types is of great importance. An
accurate prediction of different tumor types provides better treatment and may minimize the …