# 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  
This section presents the performances of hybrid systems based on hybrid CNN features for analyzing blood micrographs for diagnosing two acute leukemia datasets. The mechanism of action of this technique is to improve microscopic blood images, then segment the WBC cell region and isolate it from the rest of the main blood components. DenseNet121, ResNet50, and MobileNet models received a region of interest (WBC cells), extracted feature maps, and then selected the important features and deleted duplicates by PCA. The deep feature maps between CNN models were serially merged as follows: DenseNet121-ResNet50, ResNet50-MobileNet, DenseNet121-MobileNet, and DenseNet121-ResNet50-MobileNet. The hybrid CNN features were fed to RF and XGBoost classifiers to classify them accurately. Figure 13: Results of the XGBoost 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-XGBoost. (**b**) ResNet50-XGBoost. (**c**) MobileNet-XGBoost. The RF and XGBoost classifiers with CNN fusion features yielded superior results for classifying the C-NMC 2019 and ALL-IDB2 acute leukemia datasets. First, for the C-NMC 2019 dataset, RF and XGBoost classifiers with CNN-fused features achieved promising results as shown in Table 7 and Figure 14. The RF classifier with fusion features of DenseNet121-ResNet50 achieved an AUC of 98.1%, accuracy of 97.7%, sensitivity of 97%, precision of 97.75%, and specificity of 96.8%. The RF classifier with fusion features of ResNet50-MobileNet achieved an AUC of 97.4%, accuracy of 97.3%, sensitivity of 97.15%, precision of 96.95%, and specificity of 97.35%. In contrast, the RF classifier with fusion features of DenseNet121-MobileNet achieved an AUC of 98.55%, accuracy of 98.5%, precision of 98.4%, and specificity of 98.25%. The RF classifier with fusion features of DenseNet121-ResNet50-MobileNet achieved an AUC of 99.1%, accuracy of 98.8%, sensitivity of 98.45%, precision of 98.7%, and specificity of 98.85%. In contrast, the XGBoost classifier with fusion features of DenseNet121-ResNet50 achieved an AUC of 97.05%, accuracy of 96.3%, sensitivity of 96.1%, precision of 95.45%, and specificity of 95.8%. The XGBoost classifier with fusion features of ResNet50-MobileNet achieved an AUC of 97.85%, accuracy of 97.8%, precision of 97.15%, and specificity of 97.35%. In contrast, the XGBoost classifier with fusion features of DenseNet121-MobileNet achieved an AUC of 98.85%, accuracy of 98.1%, sensitivity of 97.8%, precision of 97.85%, and specificity of 97.95%. The XGBoost classifier with fusion features of DenseNet121-ResNet50-MobileNet achieved an AUC of 98.55%, accuracy of 98.2%, sensitivity of 98.15%, and specificity of 98.15%. Second, for the ALL-IDB2 dataset, RF and XGBoost classifiers with CNN-fused features achieved promising results. The RF classifier with fusion features of DenseNet121-ResNet50, ResNet50-MobileNet, DenseNet121-MobileNet, and DenseNet121-ResNet50-MobileNet achieved accuracy, AUC, sensitivity, precision, and specificity of 100%.