PSO-ELM Optimization Algorithm for Gastroscopic Image Classification

In medical image diagnosis, the effectiveness of pattern recognition methods is an important factor in the accurate diagnosis and evaluation of diseases. In recent years, image acquisition has been significantly improved, with devices acquiring data at faster rates and higher resolutions, but the lack of an effective image diagnosis method for gastric diseases will not meet the requirements of clinical applications if its diagnostic accuracy is not high. Therefore, this paper proposes to use particle swarm optimization to improve ELM to achieve effective recognition of gastroscopic images and provide an effective intelligent diagnosis method for medical disease diagnosis, and PSO-ELM obtains better classification performance in the experimental results of this paper. The classification accuracy was improved from 85% to 95%, the precision rate had 86.67% to 95.83%, providing an intelligent method to effectively improve the diagnosis of gastric diseases.