Deep Learning Assisted Smartphone-based Quantitative Microscopy for Label-free Hematological Analysis

Abstract

Hematologists evaluate alterations in blood cell enumeration and morphology to confirm the peripheral blood smear findings through manual microscopic examination. However, routine peripheral blood smear analysis is both time-consuming and labor-intensive. Here, we propose a smartphone-based autofluorescence microscopy (Smart-AM) system for imaging label-free blood smears at sub-cellular resolution and performing hematological analysis. Smart-AM enables rapid, high-quality, and label-free visualization of morphological features of different blood cells (leukocytes, erythrocytes, and thrombocytes) and abnormal variations in blood cells. Moreover, assisted with deep learning algorithms, this technique can automatically detect and classify different leukocytes with high accuracy, and transform the autofluorescence images into virtual Giemsa-stained images maintaining significant cellular features. The proposed technique is portable, cost-effective, and user-friendly, making it significant for broad point-of-care applications.