Method for establishing and using immunofixation electrophoresis pattern automatic identification model

Abstract

The invention discloses a method for establishing and using an immunofixation electrophoresis pattern automatic identification model, which needs to establish a model and train the model, and comprises the following steps of: a) preparing data; b) cleaning data; c) preprocessing data; d) segmenting data; e) establishing a model, extracting protein electrophoresis zone image features by a CNN network, and splicing the protein electrophoresis zone image features to form sequence features; f) carrying out data training and training an LSTM model, and g) storing the obtained model, and predictingthe IFE classification result through the trained LSTM model after the depth model is built. The invention provides a deep learning method for IFE graph automatic recognition based on end to end, therunning speed is high and the accuracy is high.

Description

technical field [0001] The invention relates to the field of deep learning, in particular to a method for establishing and using an automatic recognition model of immunofixation electrophoresis. Background technique [0002] Immunofixation electrophoresis (IFE) is an operation including agar gel protein electrophoresis and immunoprecipitation. Serum IFE can detect IgG, IgM, IgA, etc. and κ light chain, λ light chain. The principle is to perform zone electrophoresis on the sample on the agar plate, and after separation, it is covered with antiserum, including anti-κ light chain, anti-λ light chain, anti-μ heavy chain, anti-δ heavy chain, anti-γ heavy chain, and anti-ε heavy chain chain and anti-α heavy chain antiserum, when the antibody combines with monoclonal Ig in a certain zone, an immune complex precipitation can be formed, which can be adsorbed and fixed, and then rinsed and stained to present a thick and narrow colored zone. The technique is immunofixation electropho...

AI Technical Summary

Problems solved by technology

This method has high requirements for personnel, is time-consuming and labor-intensive, and has large classification deviations, resulting in low classification accuracy and repeatability, that is, different personnel may give different classification results for the same picture.

[0004] The best method that has been published is to first use the convolutional neural network to extract the features of the IFE graph, and then use the machine learning method to classify. This method is not an end-to-end structural design, so the running speed is slow and the real-time performance cannot be guaranteed.

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