Spectral data processing method and device, storage medium and electronic equipment

Abstract

The invention discloses a spectral data processing method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring a to-be-detected spectral sample and a known disease type spectral sample; calculating the to-be-detected spectrum sample and the known disease type spectrum sample according to a pre-trained convolutional neural network encoder to obtain a to-be-detected spectrum sample embedding function and a known disease type spectrum sample embedding function; calculating the cosine similarity between the to-be-detected spectrum sample embedding function and the known disease type spectrum sample embedding function; calculating according to the cosine similarity to obtain an attention matrix; and calculating according to the attention matrix to obtain a detection result of the to-be-detected spectrum sample. By implementing the method, the attention matrix is adopted, detection of the spectrum sample to be detected can be realized by adopting several samples for each type of known disease types, the training process can be accelerated, and the computing power utilization efficiency can be improved. The problem that a large number of samples are needed by adopting a deep learning mode is avoided.

Description

technical field [0001] The invention relates to the technical field of spectral data processing, in particular to a spectral data processing method, device, storage medium and electronic equipment. Background technique [0002] Surface-enhanced Raman scattering (SERS), as an optical inelastic scattering phenomenon, can significantly enhance the Raman signal by generating local-surface plasmon resonance (LSPR) on the surface of metal nanoparticles ( up to 15 orders of magnitude) for sample detection. Currently, SERS is an ultra-sensitive analytical technique that can detect target concentrations as low as single molecules. Among them, the research on the detection of tumor markers based on SERS sensor technology, including circulating tumor cells, DNA / RNA, enzymes, and partial antigens, has achieved satisfactory results in cancer screening. [0003] Clinically, as a routine non-invasive cancer diagnosis method, direct serological detection has the advantages of convenient u...

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Problems solved by technology

[0004] In view of this, the embodiment of the present invention provides a spectral data processing method, device, storage medium and electronic equipment, so as to solve the technical problem in the prior art that the sampling convolution neural network requires a large sample size for spectral classification

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