Work

Automated grade prediction of glioma patients based on magnetic resonance imaging and a random forests approach

Year: 2016

Type: conference-paper

Abstract: An automated brain tumour classification method is presented which is able to distinguish between low-grade and high-grade glioma on conventional MRI scans. Per patient, 208 quantitative features are extracted from a manually annotated brain tumour database of 274 patients. These features were then used to train a Random Forests classification algorithm. We achieved a high-grade prediction sensitivity of 85.5% and specificity of 83.3%, with a global accuracy of 85.0%.

Source: Ghent University Academic Bibliography (Ghent University)

Authors: Bonte, Stijn, Goethals, Ingeborg, Van Holen, Roel

Language: English

Topic: Brain Tumor Detection and Classification

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7026764937

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