Machine learning of diffraction image patterns for accurate classification of cells modeled with different nuclear sizes

Measurement of nuclear‐to‐cytoplasm (N:C) ratios plays an important role in detection of atypical and tumor cells. Yet, current clinical methods rely heavily on immunofluroescent staining and manual reading. To achieve the goal of rapid and label‐free cell classification, realistic optical cell models (OCMs) have been developed for simulation of diffraction imaging by single cells. A total of 1892 OCMs were obtained with varied nuclear volumes...