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

TL;DR

This paper presents a deep learning approach using Faster RCNNs for automated detection and counting of white blood cells in microscopic images, enhancing accuracy and speed for microfluidic point-of-care devices.

Contribution

It introduces a transfer learning-based deep neural network method for live blood cell detection, improving upon traditional techniques in speed and accuracy.

Findings

Deep learning significantly improves detection accuracy.

Automated analysis outperforms conventional methods.

Potential for integration into point-of-care devices.

Abstract

Automated in-vitro cell detection and counting have 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 technologies, in-vitro live cell analysis has been one of the critical tasks for both research and industry communities. However, it is a great challenge to obtain and then predict the precise information of live cells from numerous microscopic videos and images. In this paper, we investigated in-vitro detection of white blood cells using deep neural networks, and discussed how state-of-the-art machine learning techniques could fulfil the needs of medical diagnosis. The approach we used in this study was based on Faster Region-based Convolutional Neural Networks (Faster RCNNs), and a transfer learning process was applied to apply this technique to the microscopic detection of blood cells. Our experimental results demonstrated that fast and efficient analysis of blood cells 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.