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. 2023 Jan 23;154:106567. doi: 10.1016/j.compbiomed.2023.106567

Table 6.

Performance of our method against state-of-the-art methods.

Ref Dataset Method Performance
Acc. Sen. Spe. AUC
Our proposed method 156 patients (56 COVID-19 and 100 CAP)
  • -

    Lung segmentation

  • -

    MIP

 0.970 0.971 0.968 0.986
HU et al., 2022 [4] 450 patient scans (150 of COVID-19, CAP and NP)
  • -

    Lung segmentation

  • -

    Weakly supervised multi-scale learning

 0.891 0.870 0.862 0.906
Qi et al., 2022 [13] 157 patients (57 COVID-19 and 100 CAP)
  • -

    Lung segmentation

  • -

    Selection of slices with lesions

  • -

    Slice-level prediction

  • -

    Patient-level prediction

 0.971 0.959 0.981 0.992
Ibrahim et al., 2022 [29] 2984 patients (COVID-19: 1396; non-COVID-19: 1588)
  • -

    VGGNet

  • -

    Convolutional deep belief network

  • -

    High-resolution network

 0.95 0.95 0.96
Erdal et al., 2022 [55] 2496 CT scans (1428 COVID‐19 and 1068 CAP)
  • -

    Deep CNN for feature extraction

  • -

    SVM classification

 0.932 0.858 0.993 0.984
Xu et al., 2022 [56] 515 patients (204 COVID-19 and 311 CAP)
  • -

    Multi-instance learning

  • -

    LSTM

 – 0.862 0.980 0.956
Li et al., 2022 [61] 4352 CT scans (1292 COVID-19, 1735 CAP, and 1325 non-pneumonia)
  • -

    Lung segmentation

  • -

    2D local and 3D global representative features

 – 0.885 0.940 0.955
Zhu et al., 2022 [58] 2522 patients (1495 COVID-19 and 1027 CAP)
  • -

    Semi-supervised strategy

  • -

    Multi-view fusion method

  • -

    Pairwise constraint regularization

 0.920 0.931 0.904 0.963
Qi et al., 2021 [12] 241 patients (COVID-19: 141; CAP: 100)
  • -

    Multi-instance learning

  • -

    Deep features extracted by ResNet-50

 0.959 0.972 0.941 0.955
Javaheri et al., 2021 [57] 335 CT scans (111 COVID-19, 115 CAP, 109 Normal)
  • -

    Training a subset of the control dataset model

  • -

    Feeding all the datasets into the trained model

 0.933 0.909 1.00 0.940
Basset et al., 2021 [60] 305 CT scans (169 COVID-19; 60 CA; 76 Normal)
  • -

    Lung segmentation

  • -

    EfficientNet-b7 for features

  • -

    Attention modules learn multi-scale features

 0.968 – – 0.988
Ouyang et al., 2020 [23] - 3645 CT images (COVID-19: 2565; CAP: 1080)
  • -

    Lung segmentation

  • -

    3D ResNet-34

  • -

    Attention module and ensemble learning

 0.875 0.869 0.901 0.944
Song et al., 2020 [59] 201 CT images (COVID-19: 98; non-COVID-19: 103)
  • -

    BigBiGAN framework

  • -

    Linear classifier

 – 0.920 0.910 0.972