Lung anomaly detection from respiratory sound database (sound signals).

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.

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Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.