Audio analysis with convolutional neural networks and boosting algorithms tuned by metaheuristics for respiratory condition classification

IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-12-01 DOI:10.1016/j.jksuci.2024.102261
Safet Purkovic , Luka Jovanovic , Miodrag Zivkovic , Milos Antonijevic , Edin Dolicanin , Eva Tuba , Milan Tuba , Nebojsa Bacanin , Petar Spalevic
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引用次数: 0

Abstract

In contemporary medical research, respiratory disorders have become a primary focus. Improving patient outcomes for any medical condition largely depends on early identification and prompt treatment. Traditionally, medical professionals diagnose respiratory diseases by auscultating a patient’s breathing. However, this method has inherent limitations, as it may not enable physicians to accurately identify every respiratory condition. This study explores the potential of using convolutional neural networks (CNNs) in conjunction with audio analysis for the identification of respiratory problems. This work proses a novel two-tier framework that integrates CNNs with extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost) models to classify respiratory conditions. Additionally, modern optimization techniques are employed to enhance classification efficiency, recognizing the significant impact that appropriate hyperparameter tuning has on machine learning (ML) and deep learning (DL) performance. This research introduces a modified version of particle swarm optimization (PSO) tailored to meet the specific needs of ML and DL tuning. The proposed approach is validated using a real-world clinical dataset. Two studies, both based on mel spectrograms of patient breathing patterns, were conducted: the first aimed at determining whether patients have respiratory conditions (binary classification), while the second employed the same data structure for multi-class classification. In both scenarios, advanced optimizers were utilized to optimize model architecture and training settings. Under identical testing conditions, the proposed PSO metaheuristic achieved an accuracy of 98.14% for respiratory condition detection in binary classification and a slightly lower accuracy of 81.25% for specific condition identification in multi-class classification.
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来源期刊
CiteScore
10.50
自引率
8.70%
发文量
656
审稿时长
29 days
期刊介绍: In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.
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