Pneumonia remains a significant cause of child mortality, particularly in developing countries where resources and expertise are limited. The automated detection of Pneumonia can greatly assist in addressing this challenge. In this research, an XOR based Particle Swarm Optimization (PSO) is proposed to select deep features from the second last layer of a RegNet model, aiming to improve the accuracy of the CNN model on Pneumonia detection. The proposed XOR PSO algorithm offers simplicity by incorporating just one hyperparameter for initialization, and each iteration requires minimal computation time. Moreover, it achieves a balance between exploration and exploitation, leading to convergence on a suitable solution. By extracting 163 features, an impressive accuracy level of 98% was attained which demonstrates comparable accuracy to previous PSO-based methods. The source code of the proposed method is available in the GitHub repository.

翻译：肺炎仍是导致儿童死亡的主要原因，尤其在资源和专业知识匮乏的发展中国家尤为突出。自动化肺炎检测可有效应对这一挑战。本研究提出一种基于异或（XOR）的粒子群优化算法（PSO），从RegNet模型倒数第二层中筛选深度特征，旨在提升CNN模型在肺炎检测中的准确率。所提出的XOR-PSO算法仅需通过单个超参数进行初始化，且每次迭代所需计算时间极少，具有简洁性优势。此外，该算法实现了探索与利用的平衡，从而能够收敛至合适解。通过提取163个特征，模型达到了98%的惊人准确率，与先前基于PSO的方法相比展现了相当的性能。所提方法的源代码已存入GitHub仓库。