Table 7 Comparison of dense dropout deep learning models for cancer detection performance
Model | Precision | ||||
|---|---|---|---|---|---|
Adenocarcinoma | Large-Cell | Normal | Squamous cell | Average | |
EffiecientNetB3-Dense-Dropout | 0.87 | 0.76 | 0.85 | 1.00 | 0.87 |
ResNet-50-Dense-Dropout | 0.91 | 0.9 | 0.92 | 1.00 | 0.93 |
ResNet-101-Dense-Dropout | 0.96 | 0.8 | 1.00 | 1.00 | 0.94 |
Score-level fusion model | 0.92 | 0.9 | 0.92 | 1.00 | 0.94 |
Model | Recall | ||||
Adenocarcinoma | Large-Cell | Normal | Squamous cell | Average | |
EffiecientNet-B3-Dense-Dropout | 0.91 | 1.00 | 1.00 | 0.65 | 0.89 |
ResNet-50-Dense-Dropout | 1.00 | 0.90 | 1.00 | 0.83 | 0.89 |
ResNet-101-Dense-Dropout | 0.95 | 1.00 | 1.00 | 0.79 | 0.93 |
Score-level fusion model | 1.00 | 1.00 | 1.00 | 0.75 | 0.94 |
Model | F1-Score | ||||
Adenocarcinoma | Large-Cell | Normal | Squamous cell | Average | |
EffiecientNet-B3-Dense-Dropout | 0.89 | 0.86 | 0.92 | 0.79 | 0.87 |
ResNet-50-Dense-Dropout | 0.95 | 0.9 | 0.96 | 0.91 | 0.87 |
ResNet-101-Dense-Dropout | 0.96 | 0.89 | 1 | 0.88 | 0.93 |
Score-level fusion model | 0.96 | 0.95 | 0.96 | 0.86 | 0.93 |