# 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 Fusion Features CNN
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Classifier** & **Fusion Features** & **Classes** & **AUC \%** & **Accuracy \%** & **Sensitivity \%** & **Precision \%** & **Specificity \%** \\ \hline \multirow{8}{*}{RF} & DenseNet121- & ALL & 98.7 & 98.9 & 99.2 & 97.8 & 94.7 \\  & ResNet50 & Normal & 97.5 & 95.3 & 94.8 & 97.7 & 98.9 \\  & **average ratio** & **98.1** & **97.7** & **97** & **97.75** & **96.8** \\ \cline{2-8}  & \multirow{2}{*}{ResNet50-MobileNet} & ALL & 98.4 & 98.1 & 98.2 & 98 & 96.4 \\  & & Normal & 96.4 & 95.7 & 96.1 & 95.9 & 98.3 \\  & & **average ratio** & **97.4** & **97.3** & **97.15** & **96.95** & **97.35** \\ \cline{2-8}  & \multirow{2}{*}{\begin{tabular}{c} DenseNet121- \\ MobileNet \\ \end{tabular} } & ALL & 98.9 & 99.2 & 98.8 & 98.6 & 97.4 \\  & & Normal & 98.2 & 97.1 & 97.3 & 98.2 & 99.1 \\  & **average ratio** & **98.55** & **98.5** & **98.05** & **98.4** & **98.25** \\ \cline{2-8}  & \multirow{2}{*}{\begin{tabular}{c} DenseNet121- \\ ResNet50-MobileNet \\ \end{tabular} } & ALL & 99.5 & 99.3 & 99.1 & 98.9 & 98.4 \\  & & Normal & 98.7 & 97.6 & 97.8 & 98.5 & 99.3 \\  & **average ratio** & **99.1** & **98.8** & **98.45** & **98.7** & **98.85** \\ \hline \multirow{8}{*}{ XGBoost} & DenseNet121- & ALL & 97.9 & 96.7 & 97.4 & 97.8 & 94.5 \\  & & Normal & 96.2 & 95.4 & 94.8 & 93.1 & 97.1 \\ \cline{1-1}  & **average ratio** & **97.05** & **96.3** & **96.1** & **95.45** & **95.8** \\ \cline{1-1} \cline{2-8}  & \multirow{2}{*}{\
\begin{tabular}{c} ResNet50-MobileNet \\ \end{tabular} } & ALL & 98.1 & 98 & 98.2 & 98.5 & 96.8 \\  & & Normal & 97.6 & 96.8 & 97.4 & 95.8 & 97.9 \\  & **average ratio** & **97.85** & **97.6** & **97.8** & **97.15** & **97.35** \\ \cline{1-1} \cline{2-8}  & \multirow{2}{*}{
\begin{tabular}{c} DenseNet121- \\ MobileNet \\ \end{tabular} } & ALL & 99.2 & 98.7 & 98.7 & 98.5 & 96.8 \\  & & Normal & 98.5 & 96.8 & 96.9 & 97.2 & 99.1 \\  & **average ratio** & **98.85** & **98.1** & **97.85** & **97.95** \\ \hline \multirow{8}{*}{ XGBoost} & ALL & 99.9 & 98.6 & 99.2 & 98.8 & 97.4 \\  & & Normal & 98.1 & 97.5 & 96.8 & 96.9 & 98.9 \\ \cline{1-1}  & **average ratio** & **98.55** & **98.2** & **98** & **97.85** & **98.15** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of the RF and XGBoost classifiers based on fusion features of the CNN models of the C-NMC 2019 dataset for the diagnosis of acute leukemia. In contrast, the XGBoost classifier with fusion features of DenseNet121-ResNet50, ResNet50-MobileNet, DenseNet121-MobileNet, and DenseNet121-ResNet50-MobileNet achieved accuracy, AUC, sensitivity, precision, and specificity of 100%. When evaluating dataset systems C-NMC 2019: The RF classifier with fusion CNN features produces a confusion matrix, as shown in Figure 15. First, DenseNet121-ResNet50-RF attained an accuracy of 98.9% and 95.3% for ALL and normal classes. Second, ResNet50-MobileNet-RF attained an accuracy of 98.1% and 95.7% for ALL and normal classes. Third, DenseNet121-MobileNet-RF attained an accuracy of 99.2% and 97.1% for ALL and normal classes. Fourth, DenseNet121-ResNet50-MobileNet-RF attained an accuracy of 99.3% and 97.6% for ALL and normal classes. The XGBoost classifier with fusion CNN features produces a confusion matrix, as shown in Figure 16. First, DenseNet121-ResNet50-XGBoost achieved an accuracy of 96.7% and 95.4% for ALL and normal classes. Second, ResNet50-MobileNet-XGBoost achieved an accuracy of 98% and 96.8 for ALL and normal classes. Third, DenseNet121-MobileNet-XGBoost achieved an accuracy of 98.7% and 96.8% for ALL and normal classes. Fourth, DenseNet121-ResNet50-MobileNet-XGBoost achieved an accuracy of 98.6% and 97.5% for ALL and normal classes. When evaluating dataset systems ALL-IDB2: The RF classifier with fusion CNN features produces a confusion matrix, as shown in Figure 17. First, DenseNet121-ResNet50-RF attained an accuracy of 100% and 100% for ALL and normal classes. Second, ResNet50-MobileNet-RF attained an accuracy of 100% and 100% for ALL and normal classes.