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, wherein the data set includes a first data set and a second data set; using the deep residual network for training on the first data set, and transferring the trained network to the second data set to carry out training to obtain a target network; and inputting a to-be-classified cell image into the target network to obtain a feature map, analyzing the feature map, and outputting a classification result of the cell image. According to the method, the residual network is utilized for training on the data set, a method of transfer learning is combined, a network structure which can carry out automatic classification is created, the problem that biomedical image data sets are smaller is effectively solved, occurrence of over-fitting cases is avoided, workloads are reduced, work efficiency is improved, and a correctness rate of automatic cell classification is also effectively improved.

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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