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Abstract #1516

Automatic classification between high grade gliomas and brain metastasis using Bag-Of-Features in comparison to statistical and morphologic features

Moran Artzi1,2, Gilad Liberman1,3, and Dafna Ben Bashat1,2,4

1Functional Brain Center, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel, 2Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel, 3Department of Chemical Physics, Weizmann Institute, Rehovot, Israel, 4Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel

This study suggests a clinical decision-support tool for automatic classification of brain tumors. Classification was performed on 179 MRI patients: 81 patients with high grade-gliomas (HGG) and 98 patients with brain metastases (MET, 55 breast, 43 lung, cancer origin). The input data were Bag-Of-Features (BoF) and statistical-&-morphologic features extracted from T1WI+Gd. Classification was performed using five ensemble classifiers and results were evaluated using five-fold cross-validation. Best classification results produced accuracy=83%, sensitivity=87%, and specificity=81% for discriminating between HGG and MET using Statistical-&-morphologic features, and accuracy=79%, sensitivity=76%, and specificity=80% for discriminating between breast and lung MET using BoF + Statistical-&-morphologic features.

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