Research Article
Identification of Pneumonia Disease Applying an Intelligent Computational Framework Based on Deep Learning and Machine Learning Techniques
Table 4
Performance of all classifiers using the SqueezeNet transfer learning architecture.
| Classification model | Accuracy | Sensitivity | Specificity | AUC | F1-score | MCC |
| KNN (kā=ā5) | 95.16 | 96.44 | 91.05 | 97.80 | 0.95 | 0.73 | SVM (rbf) | 52.03 | 55.43 | 51.88 | 54.33 | 0.55 | 0.52 | SVM (linear) | 88.71 | 93.96 | 73.37 | 89.85 | 0.86 | 0.70 | AB | 90.12 | 93.13 | 81.43 | 87.34 | 0.90 | 0.74 | NB | 88.51 | 88.23 | 89.26 | 93.20 | 0.89 | 0.73 | LR | 96.24 | 97.62 | 91.94 | 99.20 | 0.96 | 0.90 | ANN | 96.97 | 97.52 | 92.99 | 99.40 | 0.97 | 0.92 |
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