Table 2.
Quantitative classification results based on traditional approaches and CNN model.
| Model | Specificity | Sensitivity | Accuracy | AUC | F1 |
|---|---|---|---|---|---|
| First-order features + AdaBoost | 71.00% | 52.02% | 67.35% | 0.66 | 0.38 |
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| |||||
| Texture features + SVM | 66.80% | 48.69% | 66.52% | 0.52 | 0.04 |
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| Morphological features + AdaBoost | 75.18% | 57.22% | 70.41% | 0.72 | 0.51 |
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| First-order features + Morphological features + AdaBoost | 74.73% | 54.95% | 69.29% | 0.73 | 0.49 |
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| Texture features + First-order features + AdaBoost | 70.38% | 49.87% | 66.52% | 0.65 | 0.36 |
|
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| Texture features + Morphological features + AdaBoost | 74.85% | 55.42% | 69.53% | 0.72 | 0.50 |
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| Texture features + Morphological features + First-order features + AdaBoost | 75.13% | 55.57% | 69.67% | 0.72 | 0.50 |
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| Texture features + Morphological features + First-order features + AdaBoost with LDA | 74.61% | 58.10% | 70.55% | 0.73 | 0.49 |
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| Texture features + Morphological features + First-order features + SVM | 66.93% | 37.50% | 64.53% | 0.53 | 0.15 |
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| Texture features + Morphological features + First-order features + SVM with LDA | 77.00% | 58.96% | 71.77% | 0.68 | 0.55 |
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| CNN3 | 79.22% | 63.19% | 74.44% | 0.78 | 0.60 |