Molecular imaging method and system of raman spectra based on machine learning cascade

A molecular imaging method of Raman spectra based on machine learning cascade, comprising the following steps:

• (1) attaching an untreated frozen tissue slice to a stainless steel slide such that a detection sample is obtained, and then attaching an adjacent tissue slice to a glass slide such that a control sample is obtained;
• (2) independently packaging the detection sample, storing the detection sample at 20° C. or below, conducting immunohistochemistry (IHC) staining on the control sample, obtaining an IHC image, selecting and defining a region of interest (ROI) on the IHC image, placing the stainless steel slide to which the detection sample is attached in a confocal Raman white-light field, obtaining a Raman white light image, and collecting Raman spectra of the ROI corresponding to a position of the IHC image in the Raman white light image;
• (3) inputting the collected Raman spectra into a hierarchical clustering analysis module, obtaining Raman spectra of different types of biomolecules in the ROI, excluding other types of Raman spectra according to characteristic peaks of different types of Raman spectra, and reserving pure Raman spectra of a target biomolecule in the ROI;
• (4) respectively inputting different types of obtained Raman spectra in different ROIs into a plurality of machine learning method models for training, obtaining a plurality of machine learning classification models, evaluating the plurality of machine learning classification models, selecting a machine learning classification model having optimal performance for creation of different types of Raman prediction models as a final Raman predictive imaging model, and obtaining a Raman predictive image and a quantitative score of a target biomolecule of the Raman predictive image;
• (5) evaluating similarity between the IHC image and the Raman predictive image predicted through the Raman predictive imaging model with a similarity analysis module, and evaluating correlation between quantitative scores of target biomolecules of the IHC image and the Raman predictive image, that is, evaluating reliability of the Raman predictive image of the final Raman predictive imaging model;
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• (6) preprocessing Raman spectra collected at any position of a sample to be detected, then inputting the preprocessed Raman spectra into the Raman predictive imaging model, and obtaining a Raman image and a quantitative score of a target biomolecule.