Obtaining Deep Learning Models for Automatic Classification of Leukocytes

Obtaining Deep Learning Models for Automatic Classification of Leukocytes

Pedro João Rodrigues, Getúlio Peixoto Igrejas, Romeu Ferreira Beato
Copyright: © 2020 |Pages: 32
DOI: 10.4018/978-1-7998-3095-5.ch001
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In this work, the authors classify leukocyte images using the neural network architectures that won the annual ILSVRC competition. The classification of leukocytes is made using pretrained networks and the same networks trained from scratch in order to select the ones that achieve the best performance for the intended task. The categories used are eosinophils, lymphocytes, monocytes, and neutrophils. The analysis of the results takes into account the amount of training required, the regularization techniques used, the training time, and the accuracy in image classification. The best classification results, on the order of 98%, suggest that it is possible, considering a competent preprocessing, to train a network like the DenseNet with 169 or 201 layers, in about 100 epochs, to classify leukocytes in microscopy images.
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The number of leukocytes presdataent in the blood, also known as white blood cells, provides important information regarding the state of the immune system, allowing to evaluate potential health risks. A significant change in the number of leukocytes, relative to reference values, is usually a sign that the body is affected by some type of antigen. Moreover, the variation in a particular white blood cell type is generally correlated with a specific type of antigen.

White blood cells are generally classified into 5 categories: lymphocytes, monocytes, neutrophils, eosinophils, and basophils. There is also the band designation for a specific form of the nucleus. Figure 1 shows examples of these categories.

Figure 1.

Leukocyte types (from left to right): Neutrophil, Eosinophil, Basophil, Lymphocyte, and Monocyte (Noble, 2019)


The preparation and analysis of blood samples are usually affected by deviations naturally introduced by manual operations. These difficulties can be minimized when performed by highly trained technicians. However, these tasks are labor-intensive and time-consuming and always subject to error. For these reasons, there is interest in having systems that can automatically classify with high specificity and high sensitivity.

The evolution of techniques for counting and identifying leukocytes (and blood cells in general) began in the mid-nineteenth century with the use of capillaries and slides. Over the years, several types of devices designed to count blood cells have appeared, which would later enable their classification.

The classification of blood cells has always been done by human specialists until the 1960's, since when emerged the possibility of automating this task. Firstly, through the use of optical and impedance methods and later through algorithms developed specifically for this purpose from microscopy images within the scope of computer vision and, in the last two decades, using neural networks.

Several researchers have presented papers for leukocyte identification and counting. Techniques that use classic machine learning models, in opposite to deep learning models, are built on manually selected characteristics. This approach can use shallow neuronal networks (Rezatofighi & Soltanian-Zadeh, 2011; Nazlibilek et al., 2014), Support Vector machines (SVM) (Rezatofighi & Soltanian-Zadeh, 2011; Putzu et al., 2014; Agaian et al., 2014; Alférez et al., 2016; MoradiAmin et al., 2016), Bayes classifier (Stathonikos et al., 2013; Prinyakupt & Pluempitiwiriyawej, 2015), etc. The manipulation of characteristics, prior to the classification model, may involve segmentation (Chaira, 2014), extraction and selection of features that describe the leukocyte defining region (Alférez et al., 2016). Thus, we can divide these classic processes into three main stages: segmentation, feature extraction, and classification. These approaches have the advantage of allowing the use of relatively small datasets, as the segmentation and the features used reduce the variability of the patterns delivered to the classification models. On the other hand, the segmentation performance and the lack of universality of the descriptors can limit the result achieved by the classification models. An example of this approach is found in Dan et al. (López-Puigdollers et al., 2020), where features such as SIFT (Lowe, 2004) are employed. However, the assertiveness of the classification is not very high.

The use of deep learning models tends to solve the problems presented in the approach described above, provided that the dataset is sufficiently representative of the pattern variability, associated with leukocyte optical visualization, or a transfer learning approach is employed, as is the case of the present work.

Key Terms in this Chapter

Pre-Processing: Includes processes like selection, cleaning, normalization, transformation feature extraction and transformation in order to obtain data that is more easily treatable.

Training: Determination of the best set of weights for maximizing a neural network’s accuracy.

Data Augmentation: Allows to increase the diversity on the dataset creating new patterns from small variations on the original dataset patterns.

Overfitting: Analysis that is too close to a specific dataset, that tends to fail to predict future observations or to fit additional data.

Neutrophil: The most prevalent type of leukocyte, is a type of phagocyte normally found on the bloodstream.

Features Maps: Are the output activations for a given filter produced by the convolution operator between the filter weights and the input signals.

Leukocyte: Also known as white blood cell, are involved in the protection of the body against foreign invaders and deseases.

Lymphocyte: Type of leukocyte found on the lymph. They include T and B cells as well as natural killer cells.

Eosinophil: Type of leukocyte responsible for combating parasites and infection in vertebrates.

Monocyte: They are the largest type of leukocyte, being responsible for phagocytosis, antigen presentation and cytokine production.

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