Table 1 Comparison of DL and classical machine learning techniques
Classification techniques used | TPR | TNR | PPV | NPV | TA | FPR | FNR | FDR |
|---|---|---|---|---|---|---|---|---|
Naïve Bayes | NaN | 96.64% | NaN | 99.38% | 96.38% | 3.36% | NaN | NaN |
Decision Trees | NaN | 98.82% | NaN | 98.23% | 97.20% | 1.18% | NaN | NaN |
KNN | NaN | 98.60% | NaN | 98.11% | 97.04% | 1.40% | NaN | NaN |
SVM | 68.67% | 99.16% | 76.75% | 98.72% | 83.00% | 0.84% | 31.33% | 23.25% |
Sequential Model | 90.76% | 99.63% | 91.07% | 99.2% | 92.21% | 0.36% | 9.20% | 8.90% |
GoogLeNet Model | 95.89% | 99.83% | 96.61% | 99.83% | 95.8% | 0.16% | 4.10% | 3.39% |