Sfayyih Alyaa Hamel, Sulaiman Nasri, Sabry Ahmad H
Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia.
Department of Computer Engineering, Al-Nahrain University, Al Jadriyah Bridge, 64074 Baghdad, Iraq.
J Big Data. 2023;10(1):101. doi: 10.1186/s40537-023-00762-z. Epub 2023 Jun 12.
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.
最近，深度学习和机器学习等技术在很大程度上使得对健康检查领域困难的辅助解释成为可能。通过听觉分析和医学成像，它们还提高了疾病快速和早期检测的预测准确性。由于缺乏熟练的人力资源，医疗专业人员对这种技术支持表示感激，因为它有助于他们管理更多患者。除了肺癌和呼吸系统疾病等严重疾病外，多种呼吸困难的情况正在逐渐增加并危及社会。由于早期预测和及时治疗对呼吸系统疾病至关重要，胸部X光和呼吸声音音频正被证明非常有帮助。与使用深度学习算法进行肺病分类/检测的相关综述研究相比，2011年和2018年仅进行了两项基于信号分析的肺病诊断综述研究。这项工作对使用深度学习网络的声学信号分析进行肺病识别进行了综述。我们预计，从事基于声音信号的机器学习的医生和研究人员会发现这些材料很有用。
