Stain-free artificial intelligence-assisted light microscopy for the identification of blood cells in microfluidic flow.

ABSTRACT: The identification and classification of blood cells are essential for diagnosing and managing various haematological conditions. Haematology analysers typically perform full blood counts but often require follow-up tests such as blood smears. Traditional methods like stained blood smears are laborious and subjective. This study explores the application of artificial neural networks for rapid, automated, and objective classification of major blood cell types from unstained brightfield images. The YOLO v4 object detection architecture was trained on datasets comprising erythrocytes, echinocytes, lymphocytes, monocytes, neutrophils, and platelets imaged using a microfluidic flow system. Binary classification between erythrocytes and echinocytes achieved a network F1 score of 86%. Expanding t

SUBMITTER: Hunt A 

PROVIDER: S-EPMC12391159 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature