## 4 Experimental Results
### Evaluation on Real Images  
Table 2 reports the results obtained when testing on real images and, as it can be observed, all the tested networks reported 100% performance in every calculated metric, apart from AlexNet that reached only 72.73% of accuracy. Considering the excellent classification results obtained by the networks without any explicit information on the WBC, we further investigated which features led to such results. Consequently, we computed and represented the related activation through the Grad-CAM algorithm. In particular, we reported examples of activation calculated on all the test set images. For the sake of brevity, we reported the activation maps for the ResNet-50 only, which achieved the best results in the previous test.  
Remembering that, from a clinical point of view, the correct prediction of ALL is strongly influenced by the condition of leukocytes [29], we expect that CNNs also exploit such indicators to perform the predictions. Thus, we use the healthy features of WBC to predict the healthy case and those of lymphoblasts for the ALL case. As can be seen in Figure 4, the explanations for the healthy class could be considered correct. Indeed, even if the activation areas are much bigger than the WBC areas and the WBC are not centred in the maximum activation values, this could be considered an expected behaviour for the correct classification of healthy leukocytes and the correct status grading. However, Figure 5 reports several issues on the ALL images. Specifically, let us consider the three images composed of two WBC. Figure 5a-c depict how ResNet-50 considers only one WBC in the first one and completely ignores both WBC in the remaining two. Moreover, it gives much more importance to the RBC that are not meaningful in the leukaemia diagnosis. Moreover, the situation in Figure 5d,e is even more critical because the network tends to consider only a small portion of the WBC included in the images, ignoring the majority of them.  
These critical classification issues lead us to further investigations. In particular, at first glance, it seems that the networks tend to consider the WBC as meaningful only when they are alone, as represented in the healthy images in Figure 4. On the other hand, the WBC are almost ignored when the networks classify the ALL class. This situation leads to the correct classification results, as demonstrated in Table 2. However, it is not acceptable if we want to use such a system in a real environment, where the number of classes is not limited to two (healthy and not healthy).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Acc (\%)** & **Pre (\%)** & **Rec (\%)** & **Spe (\%)** & **F1 (\%)** \\ \hline AlexNet & 72.73 & 83.33 & 71.43 & 75.00 & 76.92 \\ VGG-16 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ VGG-19 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ ResNet-18 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ ResNet-50 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ ResNet-101 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ GoogLeNet & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ Inception-v3 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ ShuffleNet & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ MobileNet-v2 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CNN performance obtained with the original ALL-IDB1 test set.