Leukemia diagnosis usually relies on multiple tests to analyze the morphological, biochemical, and genetic characteristics of blood cells. Accurate and label -free tools with the capabilities of interpreted cancer prediction and assorted diagnosing markers assure effective treatment; however, such an integrated system is currently lacking. In this study, we develop an intelligent dual -modality cell classification system with explainable machine learning by label -free measurements of the morphology features from elastic scattering images and molecular features from Raman spectra of single cells. For label -free identification of the normal granulocytes and two types of myeloid leukemia cells, the morphological features provide a classification accuracy of 96.04 % compared with 99.52 % using Raman molecular features. A high accuracy of 99.93 % is obtained by combining the morphological and molecular features. Here we reveal a total contribution of 82.49 %, 76.37 %, and 76.19 % for the morphological features compared with 17.51 %, 23.63 %, and 23.81 % for the molecular features for identifying the normal, CML, and AML cells, respectively. Integrating explainable machine learning with labelfree dual -modality systems represents a promising tool for single -cell classification, which is foreseen to have great clinical applications by offering morphological and molecular information, reliable decision -making, accuracy, and automation.
