MACHINE LEARNING IN MEDICINE: TRANSFORMING PULMONARY DISEASE DIAGNOSIS WITH LUNG SOUND ANALYSIS

Abstract

Lung sound signals are vital in clinical auscultation, providing a wealth of physiological information essential for diagnosing and monitoring human health. Currently, clinical diagnosis of lung diseases relies primarily on subjective judgments by physicians based on their experience in identifying patients' lung sounds. However, this subjective approach may lead to missed diagnoses or misdiagnoses of pulmonary conditions. While chest X-rays and lung function tests are widely used, they pose potential harm to the human body. Lung sound auscultation, which is non-invasive and harmless, offers an alternative method for diagnosing pulmonary diseases. Leveraging the advancements in computer science, utilizing machine learning techniques for recognizing and classifying lung sound signals is emerging as a critical research direction to assist healthcare professionals in diagnosing pulmonary conditions.