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Computer Assisted Classification of Brain Tumors

  • 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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Metadaten
Author:Norbert Röhrl, José R. Iglesias-Rozas, Galia WeidlORCiD
URL:https://link.springer.com/chapter/10.1007/978-3-540-78246-9_7
DOI:https://doi.org/10.1007/978-3-540-78246-9_7
Parent Title (German):Data Analysis, Machine Learning and Applications
Publisher:Springer
Document Type:Part of a Book
Language:English
Year of Completion:2007
Release Date:2023/12/07
Tag:brain; brain tumor; tumor
GND Keyword:Hirntumor
First Page:55
Last Page:60
Urheberrecht:1
Institutes:Einrichtungen / Kompetenzzentrum Künstliche Intelligenz
research focus :Intelligent Systems / Artifical Intelligence and Data Science
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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