Leukocyte Formula Elements Determination in Blood Smear Images using Deep Learning Techniques

Human blood leucocyte formula analysis is a common laboratory test performed among the first to assess human health. Concurrently, blood cell recognition is a timeconsuming and error-prone procedure Existing systems that automate the process of leukocyte counting are related to the operations of localisation of cells in the blood sample image, and further, to the operation of classification of the selected cells. The existing methods and algorithms for recognising cells in a blood sample are prone to a large number of errors and are computationally expensive. In this paper it is proposed to divide the task into two stages - the actual classification of blood cells into three classes of erythrocytes, leukocytes and platelets, with further specification of the type of leukocyte. The developed software system is based on microservice architecture and includes separate neural network models for each of the steps. The proposed method allowed the use of more compact neural network models, which accelerated the recognition process without loss of accuracy.