  Label-free cell classification is advantageous for supplying pristine cells
for further use or examination, yet existing techniques frequently fall short
in terms of specificity and speed. In this study, we address these limitations
through the development of a novel machine learning framework, Multiplex Image
Machine Learning (MIML). This architecture uniquely combines label-free cell
images with biomechanical property data, harnessing the vast, often
underutilized morphological information intrinsic to each cell. By integrating
both types of data, our model offers a more holistic understanding of the
cellular properties, utilizing morphological information typically discarded in
traditional machine learning models. This approach has led to a remarkable
98.3\% accuracy in cell classification, a substantial improvement over models
that only consider a single data type. MIML has been proven effective in
classifying white blood cells and tumor cells, with potential for broader
application due to its inherent flexibility and transfer learning capability.
It's particularly effective for cells with similar morphology but distinct
biomechanical properties. This innovative approach has significant implications
across various fields, from advancing disease diagnostics to understanding
cellular behavior.
