# Blob Detection and Deep Learning for Leukemic Blood Image Analysis
## 3 Related Works  
The state-of-the-art presents multiple systems devoted to leukocyte analysis, which face problems like segmentation, detection, or classification, to differentiate among the different types and distinguish if a cell is healthy or not. Here are some examples. [PERSON] [2] used arithmetic and thresholding image operations to develop an automated system for WBC nuclei localization and segmentation. [PERSON] [3] attempted to differentiate the five types of leukocytes using k-means clustering in the HSV color space for WBC segmentation and different classification models for cell differentiation. Generally, images acquired from digital microscopy suffer from the presence of uneven lighting and a brighter central area region, caused by the combination of the lens, the lamplight, and the presence of shaded areas towards the corners. In this case, local approaches for segmentation are more appropriate; [PERSON] [4] employed a local fuzzy threshold to manage the local illumination variations and the presence of noise or imprecision. Several authors also faced the problem of cell counting as well. [PERSON] [5] proposed a method based on an iterative threshold, determined from the histogram, to binarize the image. Then, the count was performed by extracting the connected components. Overlapping or adjacent cells were ignored. [PERSON] [6], on the other hand, used the distance transform in combination with the watershed algorithm to separate cell agglomerates and include them in the count. The separation worked only for small or small cell agglomerates. [PERSON] and [PERSON], instead [7,8], based their RBC and WBC counting methods on the Circular Hough Transform (CHT). [PERSON] applied the CHT to binary images obtained from the Lab color space, while [PERSON] modified the CHT to reduce the number of cell candidates by selecting the one with a higher probability. [PERSON] [9] proposed a completely different approach: a detection phase using the grey level co-occurrence matrix was applied directly to the original images without any previous segmentation. Consequently, it produced a significant amount of false positives, since it  
Figure 2: The five types of WBCs: basophil, eosinophil, neutrophil, lymphocyte, and monocyte. There exist two main groups of leukocytes: the granulocytes and the granulocytes. The granulocytes contain granules inside the cytoplasm. They are neutrophils, eosinophils, and basophils and have a multilobed nucleus. The agranulocytes include lymphocytes and monocytes; they do not contain granules and have non-lobular nuclei.  
worked without any restriction on the area of interest. Several methods for leukemia detection exist in literature and typically classify the two most common types of leukemia: Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL). In particular, [PERSON] [10] used an ensemble of classifiers, and [PERSON] [6] used an SVM model. In the last few years, neural networks have been increasingly used to face this issue: [PERSON] [11] and [PERSON] [12] employed them. At the same time, [PERSON] [13] realized a system for leukemia diagnosis using transfer learning in CNNs and SVM for classification.