Research Article
Fine-Grained Lung Cancer Classification from PET and CT Images Based on Multidimensional Attention Mechanism
Table 2
Evaluation of different attention mechanisms.
| Network architecture | Data | AUC | Balanced accuracy | Score of each category | Average score | Variance |
| MFSE-DenseNet (r = 4) | CT + PET | LUSC | 0.867 | 0.860 | 0.03 | 0.62 | LUAD | 0.876 | SCLC | 0.837 |
| MFSE-DenseNet (r = 16) | CT + PET | LUSC | 0.809 | 0.851 | 0.09 | 0.63 | LUAD | 0.774 | SCLC | 0.969 |
| MFSA-DenseNet | CT + PET | LUSC | 0.860 | 0.890 | 0.04 | 0.67 | LUAD | 0.961 | SCLC | 0.850 |
| MFSCA-DenseNet (r = 4) | CT + PET | LUSC | 0.938 | 0.920 | 0.05 | 0.72 | LUAD | 0.910 | SCLC | 0.913 |
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