Automated Blood Cell Detection and Counting via Deep Learning for Microfluidic Point-of-Care Medical Devices

Automated in - vitro cell detection and counting ha ve been a key theme for artificial and intelligent biological analysis such as biopsy, drug analysis and decease diagnosis. Along with the rapid development of microfluidics and lab - on - chip technolog ies, in - vitro live cell analysis has be en one of the critical task s for both research and industry communities. However, it is a great challenge to obtain and then predict the precis e information of liv e cells from numerous microscopic videos and images. In this paper, we investigated in - vitro detection of white blood cell s using deep neural networks, and discuss ed how state - of - the - art machine learning techniques could fulfil the needs of medical diagnos is. The approach we used in this study wa s based on Faster Region - based Convolutional Neural Networks (Faster RCNNs), and a transfer learning process wa s applied to apply this technique to the microscopic detection of blood cell s . Our experimental results demonstrated that fast and efficient analysis of blood cell s via automated microscopic imaging can achieve much better accuracy and faster speed than the conventionally applied methods, implying a promising future of this technology to be applied to the microfluidic point - of - care medical devices .