Medical Image Classification On Nct Crc He
Metriken
Accuracy (%)
F1-Score
Precision
Specificity
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | Accuracy (%) | F1-Score | Precision | Specificity |
|---|---|---|---|---|
| deep-residual-learning-for-image-recognition | 92.66 | 95.23 | 99.90 | 99.08 |
| efficientnet-rethinking-model-scaling-for | 95.59 | 97.48 | 99.89 | 99.45 |
| deep-residual-learning-for-image-recognition | 94.72 | 97.09 | 100.00 | 99.34 |
| res2net-a-new-multi-scale-backbone | 93.37 | 96.25 | 99.93 | 99.17 |
| regnet-self-regulated-network-for-image | 95.42 | 97.39 | 99.97 | 99.43 |
| densely-connected-convolutional-networks | 94.41 | 96.90 | 99.87 | 99.30 |
| resnet-strikes-back-an-improved-training | 95.46 | 97.46 | 99.91 | 99.43 |