# Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features
## 5 Discussion of the Results of the Performances of the Systems
The pre-trained CNN models on the ImageNet dataset do not achieve superior results because the ImageNet dataset on which the models are trained lacks medical images. Therefore, hybrid systems CNN-RF and CNN-XGBoost were applied, where it was noticed that the results improved and the systems achieved superior results for diagnosing the two leukemia datasets. It is noted that the accuracy is better improved when the CNN features are serially combined and classified with RF and XGBoost classifiers. For the C-NMC 2019 dataset, RF with fused features of DenseNet121-ResNet50-MobileNet reached the best accuracy for class ALL and normal with 99.3% and 97.6%, respectively. For the ALL-IDB2 dataset, the CNN-RF and CNN-XGBoost systems achieved an accuracy of 100% for both ALL and normal classes. The RF and XGBoost classifiers with fused features of CNN achieved an accuracy of 100% for ALL and normal classes. Figure 19: Displaying the results of all systems for the analysis of microscopic blood images for the C-NMC 2019 and ALL-IDB2 datasets acute leukemia at the level of each class. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Datasets** & \multicolumn{4}{c}{**C-NMC 2019**} & \multicolumn{4}{c}{**ALL-IDB2**} \\ \hline
**Techniques** & **Features** & **ALL** & **Normal** & **Accuracy \%** & **ALL** & **Normal** & **Accuracy \%** \\ \hline \multirow{3}{*}{pre-trained} & DenseNet121 & 93.4 & 83.8 & 90.3 & 92.3 & 88.5 & 90.4 \\  & ResNet50 & 94.6 & 85.7 & 91.8 & 96.2 & 88.5 & 92.3 \\  & MobileNet & 91.8 & 84.4 & 89.4 & 92.3 & 88.5 & 90.3 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Based on \\ active \\ contour \\ \end{tabular} } & DenseNet121 & 95.8 & 90.1 & 94 & 100 & 88.5 & 94.2 \\  & ResNet50 & 96 & 90.3 & 94.2 & 100 & 92.3 & 96.2 \\  & MobileNet & 95.7 & 88.6 & 93.5 & 96.2 & 96.2 & 96.2 \\ \hline \multirow{3}{*}{Hybrid} & \multirow{3}{*}{RF} & DenseNet121 & 96.1 & 92.6 & 95 & 100 & 100 & 100 \\  & ResNet50 & 96.6 & 93.4 & 95.5 & 100 & 100 & 100 \\  & MobileNet & 97.1 & 94.2 & 96.2 & 100 & 100 & 100 \\ \hline \multirow{3}{*}{Hybrid} & \multirow{3}{*}{XGBoost} & DenseNet121 & 95.8 & 92.2 & 94.2 & 100 & 100 & 100 \\  & ResNet50 & 97.7 & 90.4 & 95.4 & 100 & 100 & 100 \\  & MobileNet & 96.3 & 93.2 & 95.2 & 100 & 100 & 100 \\ \hline \multirow{3}{*}{Hybrid} & \multirow{3}{*}{RF} & DenseNet121-ResNet50 & 98.9 & 95.3 & 97.7 & 100 & 100 & 100 \\  & ResNet50-MobileNet & 98.1 & 95.7 & 97.3 & 100 & 100 & 100 \\  & DenseNet121-MobileNet & 99.2 & 97.1 & 98.5 & 100 & 100 & 100 \\  & DenseNet121-ResNet50-MobileNet & 99.3 & 97.6 & 98.8 & 100 & 100 & 100 \\  & MobileNet & 96.7 & 95.4 & 96.3 & 100 & 100 & 100 \\ \hline \multirow{3}{*}{Hybrid} & \multirow{3}{*}{XGBoost} & DenseNet121-ResNet50 & 96.7 & 95.4 & 96.3 & 100 & 100 & 100 \\  & ResNet50-MobileNet & 98 & 96.8 & 97.6 & 100 & 100 & 100 \\ \cline{1-1}  & DenseNet121-MobileNet & 98.7 & 96.8 & 98.1 & 100 & 100 & 100 \\ \cline{1-1}  & DenseNet121-ResNet50-MobileNet & 98.6 & 97.5 & 98.2 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of all proposed systems for diagnosing images of the NMC 2019 and ALL-IDB2 datasets based on hybrid technologies and fused features.