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Label-free cell classification is advantageous for supplying pristine cellsfor further use or examination, yet existing techniques frequently fall shortin terms of specificity and speed. In this study, we address these limitationsthrough the development of a novel machine learning framework, Multiplex ImageMachine Learning (MIML). This architecture uniquely combines label-free cellimages with biomechanical property data, harnessing the vast, oftenunderutilized morphological information intrinsic to each cell. By integratingboth types of data, our model offers a more holistic understanding of thecellular properties, utilizing morphological information typically discarded intraditional machine learning models. This approach has led to a remarkable98.3\% accuracy in cell classification, a substantial improvement over modelsthat only consider a single data type. MIML has been proven effective inclassifying white blood cells and tumor cells, with potential for broaderapplication due to its inherent flexibility and transfer learning capability.It's particularly effective for cells with similar morphology but distinctbiomechanical properties. This innovative approach has significant implicationsacross various fields, from advancing disease diagnostics to understandingcellular behavior.

MIML: Multiplex Image Machine Learning for High Precision Cell Classification via Mechanical Traits within Microfluidic Systems