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Pneumonia remains a significant cause of child mortality, particularly indeveloping countries where resources and expertise are limited. The automateddetection of Pneumonia can greatly assist in addressing this challenge. In thisresearch, an XOR based Particle Swarm Optimization (PSO) is proposed to selectdeep features from the second last layer of a RegNet model, aiming to improvethe accuracy of the CNN model on Pneumonia detection. The proposed XOR PSOalgorithm offers simplicity by incorporating just one hyperparameter forinitialization, and each iteration requires minimal computation time. Moreover,it achieves a balance between exploration and exploitation, leading toconvergence on a suitable solution. By extracting 163 features, an impressiveaccuracy level of 98% was attained which demonstrates comparable accuracy toprevious PSO-based methods. The source code of the proposed method is availablein the GitHub repository.

Optimized Deep Feature Selection for Pneumonia Detection: A Novel RegNet and XOR-Based PSO Approach