# Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features
## 4 Results of System Evaluation
### Results of Strategy of Machine-Learning with Features CNN
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Classifier** & **Models for Features** & **Classes** & **AUC \%** & **Accuracy \%** & **Sensitivity \%** & **Precision \%** & **Specificity \%** \\ \hline \multirow{8}{*}{RF} & \multirow{3}{*}{DenseNet121} & ALL & 98.4 & 96.1 & 96.2 & 96.5 & 92.9 \\  & & Normal & 97.9 & 92.6 & 93.4 & 91.8 & 95.7 \\  & & **average ratio** & **98.15** & **95** & **94.8** & **94.15** & **94.3** \\ \cline{2-8}  & \multirow{3}{*}{ResNet50} & ALL & 97.6 & 96.6 & 97.3 & 96.9 & 92.9 \\  & & Normal & 96.9 & 93.4 & 93.4 & 92.7 & 96.5 \\  & & **average ratio** & **97.25** & **95.5** & **95.35** & **94.8** & **94.7** \\ \cline{2-8}  & \multirow{3}{*}{MobileNet} & ALL & 98.2 & 97.1 & 97.2 & 97.3 & 94.2 \\  & & Normal & 95.3 & 94.2 & 94.3 & 93.8 & 97.1 \\  & & **average ratio** & **96.75** & **96.2** & **95.75** & **95.55** & **95.65** \\ \hline \multirow{8}{*}{XGBoost} & \multirow{3}{*}{DenseNet121} & ALL & 95.9 & 95.8 & 96.4 & 96.3 & 91.9 \\  & & Normal & 95.1 & 92.2 & 95.8 & 91.1 & 95.7 \\  & & **average ratio** & **95.5** & **94.7** & **96.1** & **93.7** & **93.8** \\ \cline{1-1} \cline{2-8}  & \multirow{3}{*}{ResNet50} & ALL & 98.9 & 97.7 & 97.9 & 95.6 & 90.4 \\  & & Normal & 96.4 & 90.4 & 90.4 & 94.9 & 98.2 \\  & & **average ratio** & **97.65** & **95.4** & **94.15** & **95.25** & **94.3** \\ \cline{1-1} \cline{2-8}  & \multirow{3}{*}{MobileNet} & ALL & 97.1 & 96.3 & 95.5 & 96.8 & 93.4 \\ \cline{1-1}  & & Normal & 94.9 & 93.2 & 93.2 & 92.1 & 96.1 \\ \cline{1-1}  & & **average ratio** & **96** & **95.3** & **94.35** & **94.45** & **94.75** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the RF and XGBoost classifiers based on the features of the CNN models of the C-NMC 2019 dataset for the diagnosis of acute leukemia. Figure 9: Display of results of the RF and XGBoost classifiers based on the features of the CNN models of the C-NMC 2019 dataset for the diagnosis of acute leukemia. Figure 11 shows the confusion matrix generated by the hybrid systems DenseNet121-XGBoost, ResNet50-XGBoost, and MobileNet-XGBoost. First, DenseNet121-XGBoost attained an accuracy of 95.8% and 92.2% for ALL and normal classes. Second, ResNet50-XGBoost attained an accuracy of 97.7% and 90.4% for ALL and normal classes. Third, MobileNet-XGBoost attained an accuracy of 96.3% and 93.2% for ALL and normal classes. Figure 10: Results of the RF classifier based on the features of the CNN models of the C-NMC 2019 dataset for the diagnosis of acute leukemia confusion matrix of (**a**) DenseNet121-RF. (**b**) ResNet50-RF. (**c**) MobileNet-RF. When evaluating the systems for the ALL-IDB2 dataset: the RF classifier with CNN features produces a confusion matrix, as shown in Figure 12. The hybrid systems DenseNet121-RF, ResNet50-RF, and MobileNet-RF achieved good results at each class level. First, DenseNet121-RF attained an accuracy of 100% and 100% for ALL and normal classes. Second, ResNet50-RF attained an accuracy of 100% and 100% for ALL and normal classes. Third, MobileNet-RF attained an accuracy of 100% and 100% for ALL and normal classes. Figure 11: Results of the XGBoost classifier based on the features of the CNN models of the C-NMC 2019 dataset for the diagnosis of acute leukemia confusion matrix of (**a**) DenseNet121-XGBoost. (**b**) ResNet50-XGBoost. (**c**) MobileNet-XGBoost. Figure 13 shows the confusion matrix generated by the hybrid systems DenseNet121-XGBoost, ResNet50-XGBoost, and MobileNet-XGBoost. First, DenseNet121-XGBoost attained an accuracy of 100% and 100% for ALL and normal classes. Second, ResNet50-XGBoost attained an accuracy of 100% and 100% for ALL and normal classes. Third, MobileNet-XGBoost attained an accuracy of 100% and 100% for ALL and normal classes. Figure 12: Results of the RF classifier based on the features of the CNN models of the ALL-IDB2 dataset for the diagnosis of acute leukemia confusion matrix of (**a**) DenseNet121-RF. (**b**) ResNet50-RF. (**c**) MobileNet-RF.