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Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition

Zulfiqar, Rizwana, Majeed, Fiaz, Irfan, Rizwana, Rauf, Hafiz Tayyab, BENKHELIFA, Elhadj and Belkacem, Abdelkader Nasreddine (2021) Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition. Frontiers in Medicine, 8. ISSN 2296-858X

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Abstract or description

Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typical stethoscope, which is usually considered a cheap and secure method for examining the patients. Lung disease is the third most ordinary cause of death worldwide, so; it is essential to classify the RS abnormality accurately to overcome the death rate. In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Spectrum analysis was done through Artificial Noise Addition (ANA) in conjunction with different deep convolutional neural networks (CNN) to classify the seven abnormal respiratory sounds—both continuous (CAS) and discontinuous (DAS). The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy respiratory sounds. ANA makes sound features enough reach to be identified more accurately than the respiratory sounds without ANA. The obtained results using the proposed framework are superior to previous techniques since we simultaneously considered the seven different abnormal respiratory sound classes.

Item Type: Article
Uncontrolled Keywords: respiratory sounds, abnormal respiratory sounds, continuous adventitious sounds (CASI, discontinuous adventitious sounds (DAS), deep CNN
Faculty: School of Digital, Technologies and Arts > Computer Science, AI and Robotics
Depositing User: Elhadj BENKHELIFA
Date Deposited: 29 Nov 2022 15:50
Last Modified: 29 Nov 2022 15:50
URI: https://eprints.staffs.ac.uk/id/eprint/7503

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