{'fraction_non_alphanumeric': 0.02878684030157642, 'fraction_numerical': 0.015992689056431347, 'mean_word_length': 5.317460317460317, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Applications of neural networks in the diagnosis of lymphoproliferative diseases

The work is devoted to one of the promising directions in the field of artificial intelligence - recognition of blood cells and bone marrow to differentiate leukocytes by type in the diagnosis of lymphoproliferative diseases using computer microscopy. The article presents a study using neural networks to classify cells by type.


Introduction
Modern diagnostics of hematological diseases based on the combined data of morphological research with immunophenotypical results. The study of the morphology of blood cells in blood and bone marrow smears is one of the main methods of diagnosis of acute leukemia. Important sign of the development or exacerbation of oncohematological diseases is the presence of lymphocytes with atypical or pathological morphology in the blood, even in small quantities.
Morphological classification of leukocytes is difficult to formalize, and the same cells by some researchers can be classified as normal, and others as atypical. The use of immunophenotyping to determine the differentiation of surface antigens of cells allows only to determine the total number of an immunophenotype without the possibility of assessing the "thin" morphological features of cells [1][2][3][4][5].
The development and application of new methods and means of image processing using computer microscopy to identify structural differences between normal and pathological cell elements for the diagnosis of acute leukemia is relevant.
Computer microscopy with mathematical apparatus of pattern recognition in comparison with traditional (visual analysis of microscopic images) research has immeasurably wider possibilities for the study of the structure of chromatin filaments of cell nuclei and allows to objectify the data obtained in the form of numerical indices [6][7]. Modern directions of research of application of computer microscopy are: * Obtaining additional independent objective criteria for the differential diagnosis of acute leukemia; Recently, numerous attempts have been made to automate microscopic studies to improve the accuracy of detection of young cell forms. One of these areas is the use of neural networks for the recognition of normal and atypical blood cells and bone marrow.
In a number of scientific publications it is noted that the use of cell type recognition methods for normal blood makes it possible to achieve the frequency of correct classification above 95%, while for pathological cells -myeloid and lymphoid series, this figure does not exceed 88% [8][9][10][11][12].
The article [13] analyzes the possibilities of classification of bone marrow and blood cells using the methods of "K-nearest neighbors", "Linear vector quantization", "Multilayer neural network", "support vector machine". The classification accuracy of 81%, 83%, 90% and 91% was obtained on the test sample for the presented classifiers. The database contained 108 images with 258 cells.
The aim of this work is to improve the accuracy of recognition of blood cells and bone marrow in the diagnosis of lymphoproliferative diseases using a neural classifier.
Preliminary preprocessing of initial data was carried out, consisting in normalization of values according to the following algorithm:

Results
As a result of testing, the following values of the proportion of correctly recognized cells for the corresponding classes were obtained: ("Norm" -93%," follicular lymphoma "-95%," T -ALL "-88%," B -ALL " -89%. The experiment was conducted in the following sample images of the nuclei of white blood cells: normal -1029, follicular lymphoma -1495, T-OLL -2415 and B-OLL -3004 images. The average value of correctly classified cells was 91% for the test sample.
The matrix of classification results for the test sample, formed by 25% of the sample of all cells, including 7943 images of leukocyte nuclei, is presented in table 1.

Conclusion
The paper studies the capabilities of the neural classifier for the recognition of blood cells in lymphoproliferative diseases. As a result of the experiment, the classification accuracy for the test sample under consideration is 91%.
Further development will be aimed at the analysis of classification errors and improvement of cell classification in order to determine the cells of patients with lymphoproliferative diseases.