Classifiers’ performance for detecting truncation artifacts.

The used operating points are θ = [27, 30, 40, 50, 60] s for the baseline classifier g. Note that 27 s is the optimal operating point for g, defined as the closest point to the ideal classifier with precision = recall = 1. Reported metrics for the machine learning approaches are obtained at the optimal operating points. Outperforming values for each metric are shown in bold. SVMLinear: support-vector machine with linear kernel; SVMRBF: support-vector machine with radial basis function kernel; RF: Random forests; LR: Logistic regression; Gradboost: Gradient boosting classifier. Baseline classifier: threshold on scan duration.