基于特征类内紧凑性的不平衡医学图像分类方法
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孟元, 张轶哲, 张功萱, 宋辉
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Imbalanced medical image classification based on intra⁃class compactness of features
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Yuan Meng, Yizhe Zhang, Gongxuan Zhang, Hui Song
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表5 各算法在EyePacs数据集上的对比实验结果
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Table 5 Experimental results of different algorithms on the EyePacs dataset
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| 模型 | SMOTE | KMSMOTE | UbKNN | ZC3NC |
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| Balanced ACC | Weighted⁃P | Balanced ACC | Weighted⁃P | Balanced ACC | Weighted⁃P | Balanced ACC | Weighted⁃P |
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| Avg | 46.85% | 72.24% | 47.89% | 72.49% | 48.71% | 71.94% | 51.25% | 73.27% | | ResNet18 | 46.54% | 70.77% | 48.09% | 72.26% | 48.77% | 71.05% | 51.11% | 72.54% | | ResNet50 | 46.02% | 73.91% | 46.66% | 73.01% | 48.83% | 72.52% | 51.92% | 73.94% | | ResNeXt50 | 47.46% | 74.28% | 49.72% | 74.35% | 48.19% | 72.78% | 52.34% | 74.39% | | GoogLeNet | 47.38% | 70.00% | 46.89% | 70.34% | 49.05% | 71.41% | 49.63% | 72.22% |
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