# Blob Detection and Deep Learning for Leukemic Blood Image Analysis
## 6 Conclusions  
This paper proposed a novel and efficient system for leukocyte counting and classification, providing an automated procedure to recognize the ALL, trying to overcome the typical human operator's issues, subjective opinion, or tiredness due to lengthy and repetitive tasks. Most of the works available in literature presented only methods for segmentation (e.g., [2; 4; 5; 7; 8; 9]) or classification tasks separately. Only a few proposed a complete system to overcome both detection and classification problems (e.g., [10; 11; 13]), even though the authors tested their proposed system on local datasets ([10; 11]) or on hybrid datasets [13]. Consequently, it is not possible to reproduce the same experiments of the works mentioned above, in order to make a useful performance comparison. Another novel aspect of our system was related to the detection of single cells from agglomerates without introducing any computational workload. This is a very difficult problem, often not considered or addressed by specific steps [6]. We firstly evaluated the performances of the proposed approach on the first 33 images of ALL-IDB, a public dataset for leukemia detection. The test was performed on the first 33 images because they were acquired with the same camera and presented the same illumination conditions. In particular, the results showed that it correctly detected 99.7% of the WBCs present in the first 33 images of ALL-IDB. Moreover, the system correctly identified 94.1% of the lymphoblasts, in the classification step. In both cases, the system outperformed the state-of-the-art methods based  
Figure 14: WBCs detection performances on SMC-IDB and IUMS-IDB, respectively. They are close (or equal) to 100% if \(t\) value lies in the range [0.4–0.6].  
Figure 13: WBC detection results on two images extracted from IUMS-IDB (**left**) and two from SMC-IDB (**right**).  
on computer vision techniques. WBC detection performances were also evaluated on IUMS-IDB and SMC-IDB. The accuracy results on both datasets reached 100%, matching the performances achieved by [PERSON] (2018). Further steps will be devoted to the development of the identification phase to increase the overall accuracy of the system. It is essential to study and analyze the use of new features that may be discriminant for this analysis. Moreover, the analysis of all images included in MP-IDB ([PERSON] et al., 2018), both for cell and parasite detection and for quantifying the parasitemia level, is another target for this research. As a future direction, a supervised approach to classify the detected cells can be introduced to provide the number of cell subtypes and the number of abnormal cells. In conclusion, our method proposed in this study is a very promising technique to identify ALL.  
The work described in this article is the collaborative development of all the authors. All authors have read and agreed to the published version of the manuscript.  
The Regione Autonoma della Sardegna partially supported the research in this paper with the research project \"Algorithms and Models for Imaging Science [AMIS]\" (finanziato con risorse FSC 2014-2020--Patto per lo Sviluppo della Regione Sardegna).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} WBC & White Blood Cell \\ RBC & Red Blood Cell \\ ALL-IDB & Acute Lymphoblastic Leukemia-Image Data Base \\ SMC-IDB & System, Man and Cybernetics-Image Data Base \\ IUMS-IDB & Iran University of Medical Science-Image Data Base \\ MP-IDB & Malaria Parasite Image Data Base \\ TP & True Positive \\ TN & True Negative \\ FN & False Negative \\ \end{tabular}