A review on lung disease recognition by acoustic signal analysis with deep learning networks.

Autor: Sfayyih AH; Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia., Sulaiman N; Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia., Sabry AH; Department of Computer Engineering, Al-Nahrain University, Al Jadriyah Bridge, 64074 Baghdad, Iraq.
Jazyk: angličtina
Zdroj: Journal of big data [J Big Data] 2023; Vol. 10 (1), pp. 101. Date of Electronic Publication: 2023 Jun 12.
DOI: 10.1186/s40537-023-00762-z
Abstrakt: Recently, assistive explanations for difficulties in the health check area have been made viable thanks in considerable portion to technologies like deep learning and machine learning. Using auditory analysis and medical imaging, they also increase the predictive accuracy for prompt and early disease detection. Medical professionals are thankful for such technological support since it helps them manage further patients because of the shortage of skilled human resources. In addition to serious illnesses like lung cancer and respiratory diseases, the plurality of breathing difficulties is gradually rising and endangering society. Because early prediction and immediate treatment are crucial for respiratory disorders, chest X-rays and respiratory sound audio are proving to be quite helpful together. Compared to related review studies on lung disease classification/detection using deep learning algorithms, only two review studies based on signal analysis for lung disease diagnosis have been conducted in 2011 and 2018. This work provides a review of lung disease recognition with acoustic signal analysis with deep learning networks. We anticipate that physicians and researchers working with sound-signal-based machine learning will find this material beneficial.
Competing Interests: Competing interestsThe authors declare that they have no competing interests in relation to this research, whether financial, personal, authorship or otherwise, that could affect the research and its results presented in this paper.
(© The Author(s) 2023.)
Databáze: MEDLINE
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