An Evolutionary Deep Learning for Respiratory Sounds Analysis: A Survey

The use of lung sounds in conjunction with respiratory auscultation can aid in the diagnosis of abnormalities. There is the possibility for highly developed AI combined with deep learning to automate the study of sound. A technique that utilizes neural networks and genetic algorithms to classify lung sounds is provided here. The lung sounds of people suffering from a variety of pulmonary diseases as well as healthy subjects were recorded via the chest wall. A CNN-based method for categorizing respiration sounds is proposed here. This method makes use of current breakthroughs in the field of picture classification. The Mel Frequency Cepstral Coefficients (MFCCs) play a crucial role in converting audio signals into visual representations. The level of accuracy achieved while classifying respiratory sounds (Normal, Crackles, Wheezes, Both) is far higher than was anticipated. When it comes to determining lung sounds, ML and DL, and especially EA-optimized models, are superior to conventional methods such as chest CT scanning in terms of effectiveness. In this study, EA is used to augment both ML and DL to better detect lung sounds.