Improvement effect of improved particle algorithm on BP neural network cell detection model

摘要

Due to the lack of uniform standards for pathological cell detection, it is difficult to identify. In order to improve the accuracy of pathological cell identification, this study combines the actual situation of cell detection based on traditional particle algorithm to construct a C-V model based on level set algorithm and curve evolution theory, which realizes the effective separation of different substances inside the cell. At the same time, in order to effectively extract the characteristics of cell images, this paper uses the global research method to extract the features of the research object and adopts the improved gray level co-occurrence matrix to extract the texture features, thus effectively improving the feature extraction quality. In addition, in order to study the accuracy of the algorithm model identification in this study, this paper designs a comparative experiment for performance analysis. The research shows that the proposed algorithm model has good performance, can achieve accurate recognition and feature extraction of pathological cells, has certain practical effects, and can provide theoretical reference for subsequent related research.