Pathological image classification algorithm by improved PSO-optimized SVM

doi:10.3969/j.issn.1000-1565.2020.05.013

Abstract

Abstract: In order to improve the accuracy of medical pathological image classification, a PSO parameter optimization algorithm with adaptive iterative optimization function is proposed.First, a position updating strategy based on the adaptive principle is proposed on the basis of the classical PSO algorithm.Then, an adaptive iterative optimization function is designed in the process of particle parameter optimization.The algorithm can search for optimal solution without considering the influence of speed.Finally, the PSO optimized support vector machine algorithm is used to classify and detect pathological images.The experimental results show that the classification accuracy of the algorithm is 98.5%, which is higher than that of the other two algorithms.The results of classified detection are in accordance with the results of clinical diagnosis and meet the requirements of medical research.

Key words: support vector machine, parameter optimization, pathological image, image classification

CLC Number:

TP391

DONG Bin,WANG Yuntao,JIA Linan,WANG Yanan. Pathological image classification algorithm by improved PSO-optimized SVM[J]. Journal of Hebei University (Natural Science Edition), 2020, 40(5): 543-551.

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