Research Article
Fine-Grained Lung Cancer Classification from PET and CT Images Based on Multidimensional Attention Mechanism
Table 3
Evaluation of different fusion strategies.
| Network architecture | Data | AUC | Balanced accuracy | Score of each category | Average score | Variance |
| MFSCA-DenseNet (GMU) | CT + PET | LUSC | 0.843 | 0.840 | 0.07 | 0.67 | LUAD | 0.899 | SCLC | 0.868 |
| MFSCA-DenseNet (GMU + joint optimization) | CT + PET | LUSC | 0.938 | 0.920 | 0.05 | 0.72 | LUAD | 0.910 | SCLC | 0.913 |
| MFSCA-DenseNet (connection) | CT + PET | LUSC | 0.771 | 0.801 | 0.12 | 0.63 | LUAD | 0.703 | SCLC | 0.885 |
| MFSCA-DenseNet (connection + joint optimization) | CT + PET | LUSC | 0.788 | 0.815 | 0.10 | 0.65 | LUAD | 0.759 | SCLC | 0.897 |
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