# 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  
Acute leukemia is fatal for children and young adults if not diagnosed early. The characteristics of the blasted white blood cells are similar to normal, especially in the first stage, which causes a challenge for hematologists. Therefore, computer-aided automated systems help in distinguishing infected WBC from normal. This work focused on developing systems consisting of two parts, deep and machine-learning, based on fused features. Microscopic blood images were improved for all systems and WBCs were isolated from the rest of the main blood components using the active contour method. The first strategy was to evaluate the pre-trained DenseNet121, ResNet50, and MobileNet models of the C-NMC 2019 and ALL-IDB2 acute leukemia datasets. With the C-NMC 2019 dataset, DenseNet121, ResNet50, and MobileNet achieved an accuracy of 90.3%,  
Figure 18: Results of the XGBoost classifier based on fusion features of the CNN models of the ALL-IDB2 dataset for the diagnosis of acute leukemia, confusion matrix of (**a**) DenseNet121-ResNet50, (**b**) ResNet50-MobileNet, (**c**) DenseNet121-MobileNet, and (**d**) DenseNet121-ResNet50-MobileNet. 91.8%, and 89.4%, respectively. Whereas with the ALL-IDB2 dataset, the DenseNet121, ResNet50, and MobileNet models achieved 90.4%, 92.3%, and 90.3% accuracy, respectively. The second strategy for analysis of blood micrographs for diagnosing of NMC 2019 and ALL-IDB2 datasets using DenseNet121, ResNet50, and MobileNet models was based on the active contour method. With the C-NMC 2019 dataset, DenseNet121, ResNet50, and MobileNet achieved an accuracy of 94%, 94.2%, and 93.5%, respectively. Whereas with the ALL-IDB2 dataset, the DenseNet121, ResNet50, and MobileNet models achieved 94.2%, 96.2%, and 96.2% accuracy, respectively. The third strategy was diagnosing images of two NMC 2019 and ALL-IDB2 datasets using hybrid techniques between CNN models (DenseNet121, ResNet50, and MobileNet) and RF and XGBoost classifiers based on the active contour method. With the C-NMC 2019 dataset, the DenseNet121-RF, ResNet50-RF, and MobileNet-RF systems achieved an accuracy of 100%, 100%, and 100%, respectively. In contrast, the DenseNet121-XGBoost, ResNet50-XGBoost, and MobileNet-XGBoost, and MobileNet-XGBoost achieved an accuracy of 100%, 100%, and 100%, respectively. The fourth strategy was to diagnose images of two NMC 2019 and ALL-IDB2 datasets by hybrid techniques between CNN models (DenseNet121, ResNet50 and MobileNet) and RF and XGBoost classifiers based on fused CNN features. With the C-NMC 2019 dataset, the DenseNet121-ResNet50-RF, ResNet50-MobileNet-RF, DenseNet121-MobileNet-RF, and DenseNet121-ResNet50-MobileNet-RF systems achieved accuracy of 97.7%, 97.3%, 98.5%, and 98.8%, respectively. In contrast, the DenseNet121-ResNet50-XGBoost, ResNet50-MobileNet-XGBoost, DenseNet121-MobileNet-XGBoost, and DenseNet121-ResNet50-MobileNet-XGBoost systems achieved accuracy of 96.3%, 97.6%, 98.1%, and 98.2%, respectively. With the ALL-IDB2 dataset, the DenseNet121-ResNet50-RF, ResNet50-MobileNet-RF, DenseNet121-MobileNet-RF, and DenseNet121-ResNet50-MobileNet-RF systems achieved accuracy of 100%, 100%, 100%, and 100%, respectively. In contrast, the DenseNet121-ResNet50-XGBoost, ResNet50-MobileNet-XGBoost, DenseNet121-MobileNet-XGBoost, and DenseNet121-ResNet50-MobileNet-XGBoost systems achieved accuracy of 100%, 100%, and 100%, respectively. Table 8 and Figure 19 summarize the results of the proposed systems for analyzing microscopic blood images of the two C-NMC 2019 and ALL-IDB2 acute leukemia datasets. The table shows each system's overall accuracy and each class's accuracy for each system. It is noted that the pre-trained CNN models did not produce promising results for the detection of acute leukemia. While the results improved when the images were optimized, the region of interest (WBC) was segmented and then fed to the CNN models for classification.