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. Author manuscript; available in PMC: 2019 Aug 9.
Published in final edited form as: Med Image Anal. 2019 Apr 22;55:136–147. doi: 10.1016/j.media.2019.04.009

Table 2.

Synthetic breathing artefact data classification results for mean accuracy (A), precision (P), recall (R) and balanced accuracy (BA) results. A 10-fold cross validation was used and each image was labelled once over all folds and standard deviation over folds is reported (mean ± std). All results are multiplied by 1000 and the bold font highlights the best results.

Methods A P R BA
K-Nearest Neighbours 718 ± 33 724 ± 37 721 ± 30 723 ± 36
Linear SVM 740 ± 41 737 ± 80 744 ± 48 741 ± 76
Decision Tree 707 ± 55 708 ± 42 713 ± 37 711 ± 48
Random Forests 764 ± 56 776 ± 64 781 ± 68 778 ± 61
Adaboost 768 ± 39 768 ± 54 772 ± 50 770 ± 57
Naive Bayesian 788 ± 70 790 ± 43 797 ± 68 793 ± 42
Variance of Laplacian 809 ± 43 820 ± 69 824 ± 55 822 ± 59
NIQE 897 ± 59 899 ± 71 904 ± 61 902 ± 50
Lorch et al. (2017) 896 ± 62 895 ± 47 897 ± 38 896 ± 77
3D CNN 953 ± 89 951 ± 91 961 ± 82 955 ± 70
LRCN 961 ± 41 962 ± 29 964 ± 51 963 ± 30