Abstract
The histological grade of a brain tumor is an important indicator for choosing the treatment after resection. To facilitate objectivity and reproducibility, Iglesias et al. (1986) proposed to use a standardized protocol of 50 histological features in the grading process.
We tested the ability of Support Vector Machines (SVM), Learning Vector Quantization (LVQ) and Supervised Relevance Neural Gas (SRNG) to predict the correct grades of the 794 astrocytomas in our database. Furthermore, we discuss the stability of the procedure with respect to errors and propose a different parametrization of the metric in the SRNG algorithm to avoid the introduction of unnecessary boundaries in the parameter space.
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Röhrl, N., Iglesias-Rozas, J.R., Weidl, G. (2008). Computer Assisted Classification of Brain Tumors. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_7
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DOI: https://doi.org/10.1007/978-3-540-78246-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78239-1
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