![]() Both effects stigma and requires awareness among the public and need cooperation so that it can be prevented and diagnosed so that treatment can be effective. It has been seen that both COVID-19 and TB affect health systems since both are airborne transmissible diseases, and we can diagnose them rapidly. TB and COVID-19 differ as TB is curable, but COVID-19 lacks effective anti-viral agents and drugs (Pan et al. With this intensity and demise rate of COVID-19 in the presence of lung/pulmonary diseases is increased, which is rapidly spreading among the public. It has been seen that it is critical for proper assessment of COVID-19 patients to mitigate and halt the rapid expansion of diseases across the nations. The continuous adventitious breath sounds (wheezes, stridor, and rhonchi) have a time length of > 250 ms, but discontinuous (crackle) adventitious signals have a time duration of 25 ms, according to Islama et al. ![]() There are mainly two categories of adventitious sounds, namely continuous and discontinuous. As a result, all symptoms, as stated, result in the patient’s lungs with sounds as an identifiable voice signature. Researchers even reported that COVID-19 symptoms with inadequate airflow by the vocal tract result in pulmonary and laryngological involvements in people (Asiaee et al. The repetitive dry coughs cause lungs that affect voice sound quality. Unconditionally, lung sounds also affect the scarcity of voice, affecting even shortness of breath and congestion in the upper airway. Also, when it is an indication of a critical condition, its symptoms with multiple organ failure (Kujawski et al. COVID-19 comes with an indication like fever, throat, dry cough, dyspnea, fatigue, and headache. The World Health Organization (WHO) declared COVID-19 as a global pandemic that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is rapidly spreading across more than 200 countries worldwide (Sanders et al. ![]() Patients suffering from these diseases produce adventitious sounds in their breathing cycle. One of the leading causes of death worldwide is respiratory disorder diseases (WHO 2019). The above model design achieved accurate AI-aided detection of lung diseases for light weighted edge devices. Also, combining the LSTM with Bayesian optimization improved each class’s accuracy and statistical parameters. New features are more effective in detecting lung sounds. The research supports the hypothesis that adventitious sounds have non-linear properties. Results reveal that feature sets achieved an accuracy of 94.086% for SVM, 94.684% for SVM-LSTM, and 95.699% with 95.161% for LSTM Bayesian optimization for WBS and WBP, respectively. The research employs the RALE \(^\), Inc.). ![]() SVM-LSTM analyzes these features with the Bayesian optimization algorithm model. Targeting the same, the research proposes two feature sets based on wavelet bi-spectrum and bi-phase (eight each). The characteristics of adventitious sounds contain non-linearities. Also, in this research, SVM-LSTM with the Bayesian optimization model is applied for the first time to test features of adventitious sounds. The present research targets to propose features based on the non-linearity of the adventitious sounds. The adventitious sounds heard in the respiratory cycle have non-linear characteristics. Initial biomedical signal processing techniques focused on features based on signal amplitude, so accuracy detection depends upon the signal amplitude. With the increased impact of lung diseases, it has become essential for the medical professional to leverage artificial intelligence for faster and more accurate lung auscultation. Pulmonary obstruction diseases produce adventitious sounds in the breathing cycle. ![]()
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