Xing Deng1,2, Feipeng Da1,*, Haijian Shao2,3
FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.6, pp. 1369-1383, 2023, DOI:10.32604/fdmp.2023.024836
Abstract This study employs nine distinct deep learning models to categorize 12,444 blood cell images and automatically extract from them relevant information with an accuracy that is beyond that achievable with traditional techniques. The work is intended to improve current methods for the assessment of human health through measurement of the distribution of four types of blood cells, namely, eosinophils, neutrophils, monocytes, and lymphocytes, known for their relationship with human body damage, inflammatory regions, and organ illnesses, in particular, and with the health of the immune system and other hazards, such as cardiovascular disease or infections, more in general. The results… More >
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