胸部或上身听诊长期以来被视为体格检查的有用部分，可追溯到希波克拉底时代。然而，直到1816年勒内·拉内克发明听诊器后，这一实践才得以普及，使其在卫生方面更加适用。肺部疾病是一种影响肺部和呼吸系统各个部分的疾病。肺部疾病是世界上第三大死因。根据世界卫生组织（WHO）的数据，慢性阻塞性肺疾病（COPD）、结核病、急性下呼吸道感染（LRTI）、哮喘和肺癌等五种主要呼吸系统疾病每年导致全球超过300万人死亡。呼吸音波揭示了患者肺部的重要信息。已开发了许多用于分析肺音的方法。然而，临床方法需要有资质的肺科医生来正确诊断此类信号，而且耗时较长。因此，本研究开发了一种高效的基于分数阶水循环群体算法的深度残差网络（Fr-WCSO-based DRN），用于利用呼吸音信号检测肺部异常。所提出的Fr-WCSO是通过将分数阶计算和水循环群体算法结合起来新设计的，而WCSO是水循环算法（WCA）和竞争群体优化算法（CSO）的组合。呼吸输入声音信号经过预处理，并有效提取需要进行进一步处理的重要特征。利用提取到的特征进行数据增强，以最小化拟合过度问题，提高整体检测性能。完成数据增强后，使用所提出的Fr-WCSO算法进行特征选择。最后，利用DRN进行肺部异常检测，其中DRN的训练过程是使用开发的Fr-WCSO算法执行的。所开发的方法通过考虑真阳性率（TPR）、真阴性率（TNR）和测试准确率等评估指标取得了优越的性能，分别达到了0.963（96.3%）、0.932（93.2%）和0.948（94.8%）。版权所有©2023 Elsevier Ltd.版权所有。

Chest or upper body auscultation has long been considered a useful part of the physical examination going back to the time of Hippocrates. However, it did not become a prevalent practice until the invention of the stethoscope by Rene Laennec in 1816, which made the practice suitable and hygienic. Pulmonary disease is a kind of sickness that affects the lungs and various parts of the respiratory system. Lung diseases are the third largest cause of death in the world. According to the World Health Organization (WHO), the five major respiratory diseases, namely chronic obstructive pulmonary disease (COPD), tuberculosis, acute lower respiratory tract infection (LRTI), asthma, and lung cancer, cause the death of more than 3 million people each year worldwide. Respiratory sounds disclose significant information regarding the lungs of patients. Numerous methods are developed for analyzing the lung sounds. However, clinical approaches require qualified pulmonologists to diagnose such kind of signals appropriately and are also time consuming. Hence, an efficient Fractional Water Cycle Swarm Optimizer-based Deep Residual Network (Fr-WCSO-based DRN) is developed in this research for detecting the pulmonary abnormalities using respiratory sounds signals. The proposed Fr-WCSO is newly designed by the incorporation of Fractional Calculus (FC) and Water Cycle Swarm Optimizer WCSO. Meanwhile, WCSO is the combination of Water Cycle Algorithm (WCA) with Competitive Swarm Optimizer (CSO). The respiratory input sound signals are pre-processed and the important features needed for the further processing are effectively extracted. With the extracted features, data augmentation is carried out for minimizing the over fitting issues for improving the overall detection performance. Once data augmentation is done, feature selection is performed using proposed Fr-WCSO algorithm. Finally, pulmonary abnormality detection is performed using DRN where the training procedure of DRN is performed using the developed Fr-WCSO algorithm. The developed method achieved superior performance by considering the evaluation measures, namely True Positive Rate (TPR), True Negative Rate (TNR) and testing accuracy with the values of 0.963(96.3%), 0.932,(93.2%) and 0.948(94.8%), respectively.Copyright © 2023 Elsevier Ltd. All rights reserved.