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.
Author: | Norbert Röhrl, José R. Iglesias-Rozas, Galia WeidlORCiD |
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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 |