论文标题
一种自适应和利他的基于PSO的深层特征选择方法，可从胸部X射线检测肺炎
An Adaptive and Altruistic PSO-based Deep Feature Selection Method for Pneumonia Detection from Chest X-Rays
论文作者
论文摘要
肺炎是儿童死亡率的主要原因之一，尤其是在全球收入的地区。尽管可以通过不太复杂的仪器和药物进行检测和治疗，但肺炎检测仍然是发展中国家的主要关注点。基于计算机辅助的诊断（CAD）系统可以在此类国家 /地区使用，因为其运营成本低于专业医疗专家。在本文中，我们使用深度学习的概念和一种元神父算法提出了一个从胸部X射线检测的CAD系统，以检测胸部X射线。我们首先从预先训练的RESNET50中提取深度的深度功能，该功能在目标肺炎数据集上进行了微调。然后，我们提出了一种基于粒子群优化（PSO）的特征选择技术，该技术使用基于内存的适应参数进行了修改，并通过将利他行为纳入代理人而丰富。我们将功能选择方法称为自适应和无私的PSO（AAPSO）。提出的方法成功地消除了从RESNET50模型获得的非信息性特征，从而提高了整体框架的肺炎检测能力。对公开可用的肺炎数据集进行了广泛的实验和彻底分析，确定了所提出的方法的优越性，而不是用于肺炎检测的其他几个框架。除了肺炎检测外，AAPSO还可以在某些标准的UCI数据集，用于癌症预测的基因表达数据集和COVID-19预测数据集上进行进一步评估。总体结果令人满意，从而确认AAPSO在处理各种现实生活问题方面的实用性。可以在https://github.com/rishavpramanik/aapso上找到此工作的支持源代码
Pneumonia is one of the major reasons for child mortality especially in income-deprived regions of the world. Although it can be detected and treated with very less sophisticated instruments and medication, Pneumonia detection still remains a major concern in developing countries. Computer-aided based diagnosis (CAD) systems can be used in such countries due to their lower operating costs than professional medical experts. In this paper, we propose a CAD system for Pneumonia detection from Chest X-rays, using the concepts of deep learning and a meta-heuristic algorithm. We first extract deep features from the pre-trained ResNet50, fine-tuned on a target Pneumonia dataset. Then, we propose a feature selection technique based on particle swarm optimization (PSO), which is modified using a memory-based adaptation parameter, and enriched by incorporating an altruistic behavior into the agents. We name our feature selection method as adaptive and altruistic PSO (AAPSO). The proposed method successfully eliminates non-informative features obtained from the ResNet50 model, thereby improving the Pneumonia detection ability of the overall framework. Extensive experimentation and thorough analysis on a publicly available Pneumonia dataset establish the superiority of the proposed method over several other frameworks used for Pneumonia detection. Apart from Pneumonia detection, AAPSO is further evaluated on some standard UCI datasets, gene expression datasets for cancer prediction and a COVID-19 prediction dataset. The overall results are satisfactory, thereby confirming the usefulness of AAPSO in dealing with varied real-life problems. The supporting source codes of this work can be found at https://github.com/rishavpramanik/AAPSO
