Abstract
Microscopic blood cell analysis is an important methodology for medical
diagnosis, and complete blood cell counts (CBCs) are one of the routine
tests operated in hospitals. Results of the CBCs include amounts of red
blood cells, white blood cells and platelets in a unit blood sample. It
is possible to diagnose diseases such as anemia when the numbers or
shapes of red blood cells become abnormal. The percentage of white blood
cells is one of the important indicators of many severe illnesses such
as infection and cancer. The amounts of platelets are decreased when the
patient suffers hemophilia. Doctors often use these as criteria to
monitor the general health conditions and recovery stages of the
patients in the hospital. However, many hospitals are relying on
expensive hematology analyzers to perform these tests, and these
procedures are often time consuming. There is a huge demand for an
automated, fast and easily used CBCs method in order to avoid redundant
procedures and minimize patients’ burden on costs of healthcare. In this
research, we investigate a new CBC detection method by using deep neural
networks, and discuss state of the art machine learning methods in order
to meet the medical usage requirements. The approach we applied in this
work is based on YOLOv3 algorithm, and our experimental results show the
applied deep learning algorithms have a great potential for CBCs tests,
promising for deployment of deep learning methods into microfluidic
point-of-care medical devices. As a case of study, we applied our blood
cell detector to the blood samples of COVID-19 patients, where blood
cell clots are a typical symptom of COVID-19.