Estimation of Red Blood Cell-Count using Neural Networks
David Emanuel Andersson · Lund University Publications Student Papers (Lund University) · 2020
Quantification through image analysis is used in a multitude of fields, and often requires algorithms tailored to the specific task and object that needs to be quantified. The need for flexibility means that such segmentation algorithms are quickly becoming outdated with the advent of convolutional neural networks, which can be trained to fit the specific requirements of the user. One of the fields in which quantification image analysis is of particular importance is that of hematology, where an estimation of the number of blood-cells in an image can give fast preliminary results on tests such as Complete Blood Count (CBC). In this project the use of neural networks as a means of estimating red blood-cell count in microscopic images taken at 50x magnification is investigated, and compared to a state-of-the-art segmentation algorithm designed for red blood-cell detection. The convolutional neural networks are trained to perform regression using data annotated only with the blood-cell count of the image, and the use of both synthetic data training and pre-trained models is investigated. The developed network demonstrates both higher accuracy, five times faster evaluation time, and a higher stability being able to better distinguish between overlapping red blood-cells - a major source of error for the segmentation algorithm. In addition to this, qualitative results from the network's computer vision demonstrates the network's ability to differentiate between different cell types.