White Blood Cell Classification on Raabin-WBC
98.34F1 ScoreDCENWCNet
Evaluation results
| Method | Links | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| DCENWCNetTraining time=40min 04s2025.02 | 98.34 | 98.53 | 0.9782 | 0.9817 | 98.65 | — | — | — | — | — | |
| DCENWCNetModel Category=Proposed model (Group B)2025.02 | 98.34 | 98.53 | 97.82 | 98.17 | — | — | — | — | — | — | |
| DCENWCNetOptimizer=RMSProp, Inference time (ms)=12, FLOPs (G)=1.10, Parameters (M)=6.82025.02 | 98.34 | 98.53 | 97.82 | 98.17 | — | — | — | 12 | 1.1 | 6.8 | |
| DCENWCNetOptimizer=Adam, Inference time (ms)=5, FLOPs (G)=1.10, Parameters (M)=6.82025.02 | 98.34 | 98.53 | 97.82 | 98.17 | — | — | — | 5 | 1.1 | 6.8 | |
| EfficientNetv2Model Category=Pre-trained model (Group A)2025.02 | 98.21 | — | 97.14 | 98.41 | — | — | — | — | — | — | |
| Erten et al.2025.02 | 97.79 | 97.77 | 0.9776 | 0.9783 | — | — | — | — | — | — | |
| Chen et al.2025.02 | 97.78 | 98.71 | 0.9718 | 0.9842 | — | — | — | — | — | — | |
| Han et al.2025.02 | 97.03 | 93.97 | — | — | — | — | — | — | — | — | |
| Rubin et al.2025.02 | 97 | 97 | 0.97 | 0.97 | 97 | — | — | — | — | — | |
| Rivas-Posada & Chacon-MurguiaTraining time=58min 25s2025.02 | 96.77 | 97.69 | 0.9565 | — | — | — | — | — | — | — | |
| Li et al.Training time=1hr 7min 2s2025.02 | 96.31 | 97.76 | 95.34 | 97.29 | — | — | — | — | — | — | |
| Tavakoli et al.Training time=1hr 52min 10s2025.02 | 96.14 | 94.65 | 0.9723 | 0.9507 | — | — | — | — | — | — | |
| SVMModel Category=Pre-trained model (Group A)2025.02 | 96.14 | — | 97.23 | — | — | — | — | — | — | — | |
| DenseNetModel Category=Pre-trained model (Group A)2025.02 | 95.42 | — | 94.02 | 97.07 | — | — | — | — | — | — | |
| AlexNetModel Category=Pre-trained model (Group A)2025.02 | 94.39 | 93.45 | 94.28 | 94.36 | — | — | — | — | — | — | |
| ResNet50Model Category=Pre-trained model (Group A)2025.02 | 94.28 | — | 92.87 | 96.15 | — | — | — | — | — | — | |
| Sharma et al.2025.02 | 93.5 | 95.99 | 0.82 | 0.9508 | — | — | — | — | — | — | |
| Jiang et al.2025.02 | 91.89 | 95.17 | 0.9043 | 0.934 | — | — | — | — | — | — | |
| SqueezeNet-FCMModel Category=Pre-trained model (Group A)2025.02 | 91.36 | — | 93.25 | 94.33 | — | — | — | — | — | — | |
| RCNNModel Category=Pre-trained model (Group A)2025.02 | 91.25 | 93.25 | 91.36 | 92.56 | — | — | — | — | — | — | |
| MobileNetV2Model Category=Pre-trained model (Group A)2025.02 | 89.5 | — | 86.51 | 94.78 | — | — | — | — | — | — | |
| Inception v3Model Category=Pre-trained model (Group A)2025.02 | 82.47 | — | 78.39 | 88.31 | — | — | — | — | — | — | |
| VGG19Model Category=Pre-trained model (Group A)2025.02 | 77 | — | 88.06 | 91.94 | — | — | — | — | — | — | |
| VGG16Model Category=Pre-trained model (Group A)2025.02 | 68.5 | 90.59 | 78.31 | 74.29 | — | — | — | — | — | — | |
| AlexNetInference time (ms)=11, FLOPs (G)=0.72, Parameters (M)=61.02025.02 | — | — | — | — | — | — | — | 11 | 0.72 | 61 | |
| DenseNetInference time (ms)=14, FLOPs (G)=2.9, Parameters (M)=8.02025.02 | — | — | — | — | — | — | — | 14 | 2.9 | 8 | |
| EfficientNetV2Inference time (ms)=8, FLOPs (G)=0.43, Parameters (M)=21.52025.02 | — | — | — | — | — | — | — | 8 | 0.43 | 21.5 | |
| Inception-v3Inference time (ms)=39, FLOPs (G)=5.7, Parameters (M)=23.82025.02 | — | — | — | — | — | — | — | 39 | 5.7 | 23.8 | |
| Li and Liu2025.02 | — | 97.8 | — | — | — | — | — | — | — | — | |
| MobileNetV2Inference time (ms)=11, FLOPs (G)=0.30, Parameters (M)=3.42025.02 | — | — | — | — | — | — | — | 11 | 0.3 | 3.4 | |
| RCNNInference time (ms)=14, FLOPs (G)=15.2, Parameters (M)=134.02025.02 | — | — | — | — | — | — | — | 14 | 15.2 | 134 | |
| ResNet50Inference time (ms)=28, FLOPs (G)=4.1, Parameters (M)=25.62025.02 | — | — | — | — | — | — | — | 28 | 4.1 | 25.6 | |
| SqueezeNet-FCMInference time (ms)=17, FLOPs (G)=0.83, Parameters (M)=1.52025.02 | — | — | — | — | — | — | — | 17 | 0.83 | 1.5 | |
| SVMInference time (ms)=82025.02 | — | — | — | — | — | — | — | 8 | — | — | |
| VGG16Inference time (ms)=35, FLOPs (G)=15.5, Parameters (M)=138.32025.02 | — | — | — | — | — | — | — | 35 | 15.5 | 138.3 | |
| VGG19Inference time (ms)=28, FLOPs (G)=13.6, Parameters (M)=139.62025.02 | — | — | — | — | — | — | — | 28 | 13.6 | 139.6 |