# Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features
## 4 Results of System Evaluation
### Results of CNN Model Based on Segmentation Algorithm  
This section presents the performances of DenseNet121, ResNet50, and MobileNet models based on WBC region segmentation to analyze blood micrographs diagnosing two acute leukemia datasets. DenseNet121, ResNet50, and MobileNet models received improved images and fed them into the active contour algorithm to isolate the WBC region from the rest of the blood components. The DenseNet121, ResNet50, and MobileNet models received images of regions of interest (WBC cells) 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 in its appropriate category and classified all images into ALL or normal classes.  
Based on the segmentation algorithm, 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 5 and Figure 8.  
First, for the C-NMC 2019 dataset, DenseNet121 with features of WBC region achieved an AUC of 95.95%, accuracy of 94%, sensitivity of 93.3%, precision of 93.15%, and specificity of 92.75%. ResNet50 with features of WBC region achieved an AUC of 94.9%, accuracy of 93.5%, sensitivity of 93.1%, precision of 92.7%, and specificity of 92.85%. In contrast, MobileNet with features of WBC region achieved an AUC of 96.5%, accuracy of 94.2%, sensitivity of 93.25%, precision of 93.4%, and specificity of 93.05%.  
\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{3}{*}{DenseNet121} & ALL & 95.7 & 95.8 & 96.2 & 95.4 & 89.7 \\  & & Normal & 96.2 & 90.1 & 90.4 & 90.9 & 95.8 \\  & & **average ratio** & **95.95** & **94** & **93.3** & **93.15** & **92.75** \\ \cline{2-8}  & & \multirow{3}{*}{ResNet50} & ALL & 94.1 & 95.7 & 95.8 & 94.8 & 89.4 \\  & & Normal & 95.7 & 88.6 & 90.4 & 90.6 & 96.3 \\  & & **average ratio** & **94.9** & **93.5** & **93.1** & **92.7** & **92.85** \\ \cline{2-8}  & & \multirow{3}{*}{MobileNet} & ALL & 95.9 & 96 & 96.1 & 95.5 & 90.2 \\  & & Normal & 97.1 & 90.3 & 90.4 & 91.3 & 95.9 \\  & & **average ratio** & **96.5** & **94.2** & **93.25** & **93.4** & **93.05** \\ \hline \multirow{8}{*}{ALL-IDB2} & \multirow{3}{*}{DenseNet121} & ALL & 97.1 & 100 & 99.5 & 89.7 & 88.1 \\  & & Normal & 96.8 & 88.5 & 87.8 & 100 & 99.7 \\  & & **average ratio** & **96.95** & **84.2** & **93.65** & **94.85** & **93.9** \\ \cline{1-1} \cline{2-8}  & & \multirow{3}{*}{ResNet50} & ALL & 96.8 & 100 & 99.8 & 92.9 & 91.9 \\  & & Normal & 97.4 & 92.3 & 91.8 & 100 & 99.6 \\  & & **average ratio** & **97.1** & **96.2** & **95.8** & **96.45** & **95.75** \\ \cline{1-1} \cline{2-8}  & & \multirow{3}{*}{MobileNet} & ALL & 96.8 & 96.2 & 96.3 & 95.9 & 96.4 \\  & & Normal & 96.5 & 96.2 & 95.9 & 96.4 & 96.3 \\  & & **average ratio** & **96.65** & **96.2** & **96.1** & **96.15** & **96.35** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of CNN models based on segmentation WBC of C-NMC 2019 and ALL-IDB2 datasets for acute leukemia diagnosis.  
Second, for the ALL-IDB2 dataset, DenseNet121 with features of WBC region achieved an AUC of 96.95%, accuracy of 84.2%, sensitivity of 93.65%, precision of 94.85%, and specificity of 93.9%. ResNet50 with features of WBC region achieved an AUC of 97.1%, accuracy of 96.2%, sensitivity of 95.8%, precision of 96.45%, and specificity of 95.75%. In contrast, MobileNet with features of WBC region achieved an AUC of 96.65%, accuracy of 96.2%, sensitivity of 96.1%, precision of 96.15%, and specificity of 96.35%.