Bone marrow cell classification and identification method and system based on multiple features and multiple classifiers

Abstract

The invention discloses a bone marrow cell classification and identification method and system based on multiple features and multiple classifiers. The method comprises the following steps: training and testing bone marrow cells based on three transfer learning classifiers; respectively extracting a texture feature LBP, a shape feature HOG and a color feature HSV of the bone marrow cell image data set, performing fusion to obtain a feature fusion image, and performing fusion on the three migration classifiers by using a Keras model fusion algorithm to obtain a fusion classifier; training the fusion classifier by using the feature fusion image with the best test accuracy in the single classifier to obtain a multi-feature multi-classifier fusion model with the best test accuracy; positioning and segmenting bone marrow cells in the bone marrow cell image to obtain a model application data set; and testing the data set by using the multi-feature multi-classifier fusion model with the best test effect to obtain a final model application effect. The advantages of different features and different classifiers are combined, and the test classification accuracy of the bone marrow cells is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a bone marrow cell classification and recognition method and system based on multiple features and multiple classifiers. Background technique [0002] Compared with manual recognition, computer-aided technology has many natural advantages. First, its recognition speed is fast and its efficiency is high. Generally speaking, there are few inspectors with rich experience, but there are many images of bone marrow cells. Manual identification will inevitably lead to extremely low efficiency. Good algorithms and machines can scan and recognize dozens or even hundreds of images in one minute, greatly improving efficiency. Second, the recognition accuracy is high. Some inspectors do not have rich experience, and mistakes are likely to occur during the identification process, which is not conducive to the diagnosis of diseases. As long as the computer-aided recogn...

AI Technical Summary

Problems solved by technology

When observing cells manually, it is sometimes difficult to separate them due to the adhesion between cells, which greatly reduces the efficiency of identification

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