Method,f1_CN,f1_VMD,f1_AD,recall_CN,recall_VMD,recall_AD
XGBoost,63.3±7.6,41.3±7.8,37.7±13.2,66.2±9.6,39.9±8.9,35.8±13.9
MLP,46.4±17.0,29.2±16.4,41.2±14.0,45.8±22.4,29.1±21.8,57.8±18.7
ResNet50,56.1±13.2,33.9±16.1,20.5±12.7,56.4±18.5,35.9±21.3,27.3±20.6
DenseNet121,66.7±5.4,31.6±14.4,36.5±11.0,71.1±9.2,28.4±14.5,41.0±17.5
3D ResNet18,61.9±10.6,20.4±20.4,24.0±21.1,74.9±22.3,22.3±25.3,26.6±25.6
ViT-B/16,64.3±9.3,41.9±14.6,32.3±15.3,66.5±19.6,44.2±19.8,36.3±25.4
3D Swin-T,58.3±12.2,34.7±18.4,28.0±16.3,58.7±20.7,40.9±28.4,33.0±23.9
3D HCCT,64.6±9.8,29.8±21.3,27.9±18.3,71.2±19.4,33.1±28.6,30.6±24.3
CNN-VSwinFormer-lite,54.1±18.8,27.9±14.8,24.2±16.7,58.1±26.2,28.2±22.8,33.8±27.6
DenseNet + concat,67.3±7.7,42.8±7.8,39.0±12.2,67.7±14.6,38.7±8.4,51.2±24.3
TriFuse-AD,62.3±10.1,47.4±12.7,36.7±12.0,57.2±13.1,51.1±18.0,44.0±21.0