标题： 基于RegNet和XOR的新型粒子群优化深度特征选择方法用于肺炎检测 显示英文标题

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

摘要： 肺炎仍然是儿童死亡的重要原因，尤其是在资源和专业知识有限的发展中国家。 自动检测肺炎可以大大帮助解决这一挑战。 在本研究中，提出了一种基于XOR的粒子群优化（PSO），用于从RegNet模型的倒数第二层选择深度特征，旨在提高卷积神经网络模型在肺炎检测中的准确性。 所提出的XOR PSO算法通过仅需一个超参数进行初始化而具有简单性，每次迭代所需的计算时间极少。 此外，它在探索和利用之间取得了平衡，从而收敛到一个合适的解决方案。 通过提取163个特征，达到了98%的出色准确率，这表明其准确率与之前的基于PSO的方法相当。 所提出方法的源代码可在GitHub仓库中获得。 显示英文摘要

摘要： 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.