A cell classification method and system based on deep residual network

Abstract

The invention discloses a cell classification method and system based on a deep residual network. The method includes: acquiring a data set of cell images, the data set including a first data set and a second data set; using a deep residual network The first data set is trained, and the trained network is migrated to the second data set for training to obtain a target network; the cell image to be classified is input into the target network to obtain a feature map, And analyze the feature map, and output the classification result of the cell image. The present invention uses the residual network to train the data set, and combines the method of transfer learning to create a network structure that can be automatically classified, which effectively solves the problem of small biomedical image data sets and avoids the occurrence of overfitting. It not only reduces the workload and improves the work efficiency, but also effectively improves the correct rate of automatic cell classification.

Description

technical field [0001] The invention relates to the technical field of medical image processing, in particular to a cell classification method based on a deep residual network, a storage medium and a terminal. Background technique [0002] The indirect immunofluorescence (IIF) detection technology of HEp-2 cells is mainly used for the analysis of nuclear antibody (ANA), which is derived from human epidermal cells and used for the diagnosis and treatment of some important autoimmune diseases. For example, systemic rheumatic diseases, multiple sclerosis, drug-induced lupus erythematosus, systemic lupus erythematosus, and diabetes. Since HEp-2 cells have a high ability to divide, they produce a large amount of antigens. Experts usually perform artificial nuclear antibody examination using fluorescence microscopy, which requires evaluation and estimation of fluorescence intensity and staining pattern. [0003] Currently, computer-aided diagnosis (CAD)-based systems can automat...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to provide a cell classification method and system based on a deep residual network in view of the above-mentioned defects of the prior art, aiming at solving the problem that the accuracy of the cell classification method in the prior art is not high, and using In DCNN's method of classifying cells, overfitting is prone to occur, which affects classification accuracy and other issues.

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