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

卷:10

Review

关键词: SOUND EVENT DETECTION;    DATA AUGMENTATION;    ARTIFICIAL-INTELLIGENCE;    NEURAL-NETWORK;    CLASSIFICATION;    DIAGNOSIS;    COVID-19;    MODEL;    AUSCULTATION;    SYSTEM;   

DOI  :  10.1186/s40537-023-00762-z

来源: SCIE

【 摘 要 】

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.