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

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.