# Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features
## 4 Results of System Evaluation
### Results of CNN Model  
This section presents the performances of the DenseNet121, ResNet50, and MobileNet models for analyzing microscopic blood images for diagnosing two acute leukemia datasets. The DenseNet121, ResNet50, and MobileNet models received acute leukemia images and extracted feature maps through convolutional layers. Fully connected layers received high-level feature maps and put them into single-level feature vectors. The SoftMax activation function labeled each image into its appropriate class and classified all images into ALL or normal classes.  
The DenseNet121, ResNet50, and MobileNet models yielded good results for classifying the C-NMC 2019 and ALL-IDB2 acute leukemia datasets, as shown in Table 4 and Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**DataSets** & **Models** & **Classes** & **AUC \%** & **Accuracy \%** & **Sensitivity \%** & **Precision \%** & **Specificity \%** \\ \hline \multirow{8}{*}{C-NMC 2019} & \multirow{2}{*}{DenseNet121} & ALL & 95.2 & 93.4 & 93.2 & 92.5 & 83.8 \\  & & Normal & 94.1 & 83.8 & 84.4 & 85.5 & 92.9 \\  & & **average ratio** & **94.65** & **90.3** & **88.8** & **89** & **88.35** \\ \cline{2-8}  & \multirow{2}{*}{ResNet50} & ALL & 94.7 & 94.6 & 95.2 & 92.8 & 86.1 \\  & & Normal & 95.6 & 85.7 & 85.9 & 88.2 & 94.8 \\  & & **average ratio** & **95.15** & **90.8** & **90.55** & **90.5** & **90.53** \\ \cline{2-8}  & \multirow{2}{*}{MobileNet} & ALL & 92.9 & 91.8 & 92.2 & 92.6 & 84.2 \\  & & Normal & 93.2 & 84.4 & 83.7 & 82.8 & 92.4 \\  & & **average ratio** & **93.05** & **89.4** & **87.95** & **87.7** & **88.3** \\ \hline \multirow{8}{*}{ALL-IDB2} & \multirow{2}{*}{DenseNet121} & ALL & 93.9 & 92.3 & 92.4 & 88.9 & 88.3 \\  & & Normal & 93.7 & 88.5 & 87.9 & 92 & 92.1 \\  & & **average ratio** & **93.8** & **90.4** & **90.15** & **90.45** & **90.2** \\ \cline{2-8}  & \multirow{2}{*}{ResNet50} & ALL & 92.8 & 96.2 & 96.1 & 89.3 & 87.7 \\  & & Normal & 91.9 & 88.5 & 88.4 & 95.8 & 95.9 \\  & & **average ratio** & **92.35** & **92.3** & **92.25** & **92.55** & **91.8** \\ \cline{2-8}  & \multirow{2}{*}{MobileNet} & ALL & 93.4 & 92.3 & 92.4 & 88.9 & 87.7 \\  & & Normal & 92.7 & 88.5 & 88.1 & 92 & 92.2 \\  & & **average ratio** & **93.05** & **90.4** & **90.25** & **90.45** & **89.95** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of DenseNet121, ResNet50, and MobileNet models pre-trained on two datasets, C-NMC 2019 and ALL-IDB2, for acute leukemia diagnosis.  
Figure 7: Display of results of CNN models of the C-NMC 2019 and ALL-IDB2 datasets for acute leukemia diagnosis.  
First, for the C-NMC 2019 dataset, DenseNet121 achieved an AUC of 94.65%, accuracy of 90.3%, sensitivity of 88.8%, precision of 89%, and specificity of 88.35%. ResNet50 achieved an AUC of 95.15%, accuracy of 90.8%, sensitivity of 90.55%, precision of 90.5%, and specificity of 90.53%. In contrast, the MobileNet model achieved an AUC of 93.05%, accuracy of 89.4%, sensitivity of 87.95%, precision of 87.7%, and specificity of 88.3%.  
Second, for the ALL-IDB2 dataset, DenseNet121 achieved an AUC of 93.8%, accuracy of 90.4%, sensitivity of 90.15%, precision of 90.45%, and specificity of 90.2%. ResNet50 achieved an AUC of 92.35%, accuracy of 90.3%, sensitivity of 92.25%, precision of 92.55%, and specificity of 91.8%. In contrast, MobileNet achieved an AUC of 93.05%, accuracy of 90.4%, sensitivity of 90.25%, precision of 90.45%, and specificity of 89.95%.