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Fr-WCSO- DRN: Fractional Water Cycle Swarm Optimizer-Based Deep Residual Network for Pulmonary Abnormality Detection from Respiratory Sound Signals

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Abstract

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, 0.932, and 0.948, respectively. Moreover, the testing accuracy value achieved by the Random Forest classifier, machine learning, DNN, CNN, WCSO-based HAN, and developed Fr-WCSO-based DRN is 0.753, 0.797, 0.844, 0.887, 0.929, and 0.948. While analyzing the results that are tabulated, it is clear that the developed Fr-WCSO-based DRN computed a higher TPR of 0.963, higher TNR of 0.932 using dataset-1, and higher testing accuracy of 0.948 using dataset-2, respectively. The effectual results are obtained as the model is well trained with the proposed Fr-WCSO and hence increasing the learning rate of the Deep Residual Network.

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Data Availability Statement

Availability of data and materials The datasets used and/or analyzed during the current study are available from the Respiratory Sound Database, "https://www.kaggle.com/vbookshelf/respiratory-sound-database", accessed on January 2021 and International Conference in Biomedical and Health Informatics (ICBHI), ICBHI 2017 challenge database, “https://bhichallenge.med.auth.gr/”, accessed on June 2021.on reasonable request. The codes of models are available at https://github.com/Jawad-Dar/Fractional-Water-Cycle-Swarm-Optimizer-based-deep-residual-network-for-Recognition-of-lung-abnormali.

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Acknowledgements

We are obliged for the helpful comments by the Anonymous reviewers that helped us to improve the quality of the manuscript. All persons who have made substantial contributions to the work reported in the manuscript (e.g. technical help, writing and editing assistance, general support.).

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  1. Kamal Kr. Srivastava

    Present address: Department of Computer Science and Engineering, Mansarovar Global University, Sehore, Madhya Pradesh, India

Authors and Affiliations

  1. Department of Computer Science and Engineering, Mansarovar Global University, Sehore, Madhya Pradesh, India

    Jawad Ahmad Dar

  2. Department of Computer Science and Engineering, Islamic University of Science and Technology, Awantipora, Kashmir, India

    Sajaad Ahmed Lone

Authors
  1. Jawad Ahmad Dar
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  2. Kamal Kr. Srivastava
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  3. Sajaad Ahmed Lone
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Contributions

(a). Conception and design of study: all authors contributed. (b). acquisition of data: all authors contributed. (c). analysis and interpretation of data: all authors contributed. (d). drafting of manuscript: all authors contributed. (e). Approval of the version of the manuscript to be published: all author’s consent.

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Correspondence to Jawad Ahmad Dar.

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No conflict of interest exists. We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.

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Approved by RDC of Mansarovar Global University Madhya Pradesh India.

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A preprint version of this paper is available on research square (https://www.researchsquare.com/article/rs-1082541/v1), https://doi.org/10.21203/rs.3.rs-1082541/v1, to gain feedback from the community, and start making changes in manuscript prior to peer review in a journal.

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This article is part of the topical collection “Biomedical Engineering Systems and Technologies” guest edited by Hugo Gamboa and Ana Fred.

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Dar, J.A., Srivastava, K.K. & Lone, S.A. Fr-WCSO- DRN: Fractional Water Cycle Swarm Optimizer-Based Deep Residual Network for Pulmonary Abnormality Detection from Respiratory Sound Signals. SN COMPUT. SCI. 3, 378 (2022). https://doi.org/10.1007/s42979-022-01264-0

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