## 2 Background and Related Works
### Related Works  
Several CAD systems have been proposed for the analysis of blood smear images to support either the disease identification as early as possible or to perform an automatic diagnosis. CAD systems for PBS image analysis typically consist of four main steps: image pre-processing, segmentation or detection, feature extraction and classification. Obviously not all the CAD systems exploit all the mentioned steps. This depends on the type of analysis performed and also on the type and quality of the used images. For example, the pre-processing step may not be necessary in most cases given that the images captured by new digital microscopes are of excellent quality. At the same time, methods devoted to cell counting do not need feature extraction and classification. Thus, the used pipelines are heavily influenced by the medical problem they deal with and the task they are designed for. Furthermore, some steps may be repeated more times inside a single pipeline in order to address specific issues related to the images under investigation or to perform a hierarchical analysis. To give an example, multiple segmentation steps can be used or combined with detection steps to deal with adjacent or clustered cells [32; 33; 34]. In addition, multiple segmentation or multiple classification steps might be performed to identify cells and their sub-types or separate their components. As a general rule, the more fine-grained the analysis step to be performed, the greater the need for intermediate processing steps between the input data and the output results. The visual difference between mature lymphocytes and lymphoblasts is very subtle, as shown in Figure 2b, and distinguishing them is very complex even for trained experts. The complexity of such a task was also highlighted by the recent C-NMC [35] challenge, where few authors achieved an F1-score value higher than 90%, even though the provided data set is extensive, with high-quality images presenting a single WBC centred and manually segmented. Considering that typically a whole PBS contains a considerable number of leukocytes, the analysis has always been approached with methods involving at least two steps: one dedicated to leukocyte detection/segmentation [36; 37] and one dedicated to leukocyte classification [20; 38]. In some cases the first step has been simplified, dedicating it to the detection of all cell types [24; 39; 40; 41] and leaving the separation into the various sub-types and lymphoblasts to the classification step. On the other hand, other authors have simplified the classification step into just two types (leukocytes vs. lymphoblasts or even lymphocytes and lymphoblasts) by inserting intermediate steps for cataloguing WBC to improve the final accuracy [19; 42]. However, several authors recently tried to avoid complex segmentation methods involving intense computations. In particular, they performed the classification task directly on the original images exploiting different CNN architectures [21; 22; 23]. The reported results are outstanding, achieving even 100% [21] accuracy on the well-known ALL-IDB1 data set (for more details on the data set, see Section 3.1). This approach takes advantage of the fact that ALL causes an overproduction of lymphoblasts in the PBS, which facilitates and influences the classification process. Several important considerations should be made up to this point. First, we must remember that the CNN-based approach works with resolutions lower than 300 \(\times\) 300 pixels. Therefore, as previously introduced in Section 1, a considerable amount of information is lost. Second, distinguishing between mature lymphocytes and lymphoblasts at this resolution level is almost impossible. Third and consequently, a classification system based on the original images can at most predict the overproduction of leukocytes rather than leukaemia, considering that leukocytes are indistinguishable from the lymphoblasts.