Table 10 Performance evaluation of ensemble deep learning model.
From: Brain tumor detection empowered with ensemble deep learning approaches from MRI scan images
Models | Ensembled (Inception-V3 and Xception) Deep Learning Model | |||||||
|---|---|---|---|---|---|---|---|---|
Phases | Training | Validation | ||||||
Classes | Normal | Glioma | Meningioma | Pituitary | Normal | Glioma | Meningioma | Pituitary |
Accuracy | 1.00 | 0.99 | 0.97 | 0.97 | 0.98 | 0.97 | 0.99 | 0.98 |
Miss Rate | 0.00 | 0.01 | 0.03 | 0.03 | 0.02 | 0.03 | 0.01 | 0.02 |
Sensitivity | 1.00 | 1.00 | 0.98 | 0.98 | 0.99 | 0.99 | 1.00 | 0.997 |
Specificity | 1.00 | 0.98 | 0.97 | 0.96 | 0.97 | 0.96 | 0.98 | 0.965 |
Precision | 1.00 | 0.98 | 0.96 | 0.96 | 0.96 | 0.98 | 0.98 | 0.96 |
FPR (False Positive Rate) | 0.00 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 |
FNR (False Negative Rate) | 0.00 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 |
