Advancing precision single-cell analysis of red blood cells through semi-supervised deep learning using database of patients with post-COVID-19 syndrome

Abstract

We developed a semi-supervised deep learning-based system classifying different types of red blood cells (RBCs) images based on their shape, texture, and size. Specifically, pre-training a convolutional neural network was done on over 35,000 brightfield images of RBCs acquired with an imaging flow cytometer from a post-COVID-19 patient cohort. The system utilizes object localization powered by a YOLO-inspired block for cell identification and a de-blurring CNN block based on FocalNet. A series of convolutional and fully connected layers classifies images into side-view, biconcave, elongated, and additional categories for reticulocytes and erythrocytes. Fine-tuning was done using 7,000 manually labeled brightfield images. Consequent evaluation on a test dataset of 3,000 samples yielded an accuracy of 98.2%. This system can be used for other cell analysis tasks, not requiring large fine-tuning datasets while maintaining high efficiency.