Table 1.
Performance comparison in multi-site pooling classification.
| Methods | ACC | SEN | SPE | F1 | AUC | |
|---|---|---|---|---|---|---|
| CON | Adaboost | 75.6(3.8)** | 77.0(4.4)** | 74.2(4.4)** | 76.2(3.8)** | 84.2(3.6)** |
| CON | Random Forest | 76.0(3.5)** | 81.0(3.9)o | 71.4(5.5)** | 77.4(3.5)** | 84.0(3.4)** |
| CON | SVM | 79.4(3.1)* | 80.4(3.5)o | 78.4(3.9)* | 79.6(3.3)* | 86.8(3.2)* |
| RNN | GRU_1_last | 51.6(3.6)** | 52.0(5.3)** | 51.2(4.3)** | 52.0(3.8)** | 51.2(3.6)** |
| RNN | GRU_1_ave | 77.8(3.4)** | 78.4(3.8)** | 77.0(3.5)** | 78.2(3.4)** | 86.8(3.5)* |
| RNN | GRU_2_ave | 78.0(3.9)** | 80.8(5.1)o | 76.0(4.2)** | 78.8(3.9)* | 86.8(4.1)* |
| CMLP | Multi_CNN_MLP | 77.8(3.4)** | 76.2(4.0)** | 79.2(4.8)○ | 77.2(3.4)** | 86.4(3.1)** |
| CRNN | Simple_CNN_GRU_2_ave | 80.8(3.0)○ | 80.2(4.3)○ | 82.0(3.5)○ | 80.8(3.1)○ | 89.2(2.8)○ |
| CRNN | Multi_CNN_GRU_1_ave | 80.6(3.5)○ | 80.8(4.1)○ | 80.6(4.3)○ | 80.8(3.3)○ | 88.2(3.6)○ |
| CRNN | Multi_CNN_GRU_2_ave | 81.2(3.4)○ | 81.4(4.1)○ | 81.0(4.9)○ | 81.0(3.5)○ | 88.6(3.7)○ |
| CRNN | Multi_CNN_LSTM_2_ave | 81.6(2.9)○ | 82.6(3.6)○ | 80.4(3.8)○ | 82.0(2.7)○ | 89.4(2.8)○ |
| CRNN | MsRNN(Proposed) | 83.2(3.2) | 83.1(3.7) | 83.5(3.7) | 83.3(3.2) | 90.6(3.0) |
CON: conventional classification methods; RNN: RNN-based methods; CMLP: CNN linked with multi-layer perception; CRNN: CNN-RNN based methods; SVM: Support vector machine with Gaussian kernel; LSTM: Long short-term memory network; GRU: gated recurrent unit. GRU_1: one layer of GRU; GRU_2: two-layer stacked GRU; #_last: the output of the last GRU step is connected to the next layer. #_ave: the average of the outputs of all GRU steps is connected to the next layer; SimpleCNN: Convolutional layer has fixed kernel size; Multi_CNN: Convolutional layer has different kernel size; ○ denotes that the methods have no significant difference (two-sample t-test) with the proposed. */** denote respectively that the methods are significantly worse than the proposed model with P value = .05/0.01. Details of all these mentioned architectures are shown in Supplementary file Fig. S2. The last row is our proposed method.