SVM-based glioma grading: Optimization by feature reduction analysis.
By Frank G. Zöllner, Kyrre Eeg Emblem, Lothar Rudi Schad. 2012
69 citations recorded by the source corpus.
We investigated the predictive power of feature reduction analysis approaches in support vector machine (SVM)-based classification of glioma grade. In 101 untreated glioma patients, three analytic approaches were evaluated to derive an optimal reduction in features; (i) Pearson's correlation coefficients (PCC), (ii) principal component analysis (PCA) and (iii) independent component analysis (ICA). Tumor grading was performed using a previously reported SVM approach including whole-tumor cerebral blood volume (CBV) histograms and patient age. Best classification accuracy was found using PCA at 85% (sensitivity=89%, specificity=84%) when reducing the feature vector from 101 (100-bins rCBV histogram+age) to 3 principal components. In comparison, classification accuracy by PCC was 82% (89%, 77%, 2 dimensions) and 79% by ICA (87%, 75%, 9 dimensions). For improved speed (up to 30%) and simplicity, feature reduction by all three methods provided similar classification accuracy to literature values (∼87%) while reducing the number of features by up to 98%.
Subfields: Artificial Intelligence, Neurology
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In Artificial Intelligence
- Jakob Nikolas Kather
- A. Marx
- Cleo-Aron Weis
- T. Gaiser
- Susanne Melchers
- Francesco Bianconi
- Lothar Rudi Schad
- Frank G. Zöllner
Citations in the corpus
- Multi-class texture analysis in colorectal cancer histology (2016, methodology): “Typically, these methods extract texture features first 8,12–14 , then feed the features into a classifier to predict the tissue type 9,15,16 .”